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KEEL Algorithms

In this section you may find a complete list of all the algorithms which have been included within the KEEL software tool. This list is grouped into different families and it is summarised in the following table. Please click on any item in order to navigate into the different types of algorithms.

Algorithms included in KEEL (515)
  Family    Subfamily  
  Data Preprocessing (98)Discretization (30)
Feature Selection (25)Feature Selection (22)
Evolutionary Feature Selection (3)
Training Set Selection (16)Training Set Selection (12)
Evolutionary Training Set Selection (4)
Missing Values (15)
Transformation (4)
Data Complexity (1)
Noisy Data Filtering (7)
  Classification Algorithms (248)Rule Learning for Classification (94)Crisp Rule Learning for Classification (19)
Evolutionary Crisp Rule Learning for Classification (29)
Fuzzy Rule Learning for Classification (4)
Evolutionary Fuzzy Rule Learning for Classification (19)
Nested Generalized Learning (5)
Associative Classification (7)
Decision Trees (11)
Instance Based Learning (131)Prototype Selection (40)
Evolutionary Prototype Selection (12)
Fuzzy Instance Based Learning (20)
Prototype Generation (44)
Lazy Learning (15)
Neural Networks for Classification (12)Neural Networks for Classification (10)
Evolutionary Neural Networks for Classification (2)
Support Vector Machines for Classification (3)
Statistical Classifiers (8)
  Regression Algorithms (48)Rule Learning for Regression (16)Fuzzy Rule Learning for Regression (2)
Evolutionary Fuzzy Rule Learning for Regression (11)
Decision Trees for Regression (3)
Evolutionary Postprocessing FRBS: Selection and Tuning (14)
Neural Networks for Regression (10)Neural Networks for Regression (8)
Evolutionary Neural Networks for Regression (2)
Support Vector Machines for Regression (2)
Evolutionary Fuzzy Symbolic Regression (4)
Statistical Regression (2)
  Imbalanced Classification (45)Resampling Data Space (20)Over-sampling Methods (12)
Under-sampling Methods (8)
Algorithmic Modifications for Class Imbalance (1)
Cost-Sensitive Classification (3)
Ensembles for Class Imbalance (21)
  Semi-Supervised Learning (18)Multiple-Classifier Methods (10)
Single-Classifier Methods (4)
Supervised Methods (4)
  Subgroup Discovery (7)
  Multi Instance Learning (9)
  Clustering Algorithms (1)
  Association Rules (17)
  Statistical Tests (24)Test Analysis (12)
Post-Hoc Procedures (12)Post-Hoc Procedures for 1 x N Tests (8)
Post-Hoc Procedures for N x N Tests (4)



Main Data Preprocessing

Main DISCRETIZATION
  Full Name    Short Name    Reference  
Uniform Width Discretizer UniformWidth-D H. Liu, F. Hussain, C.L. Tan, M. Dash. Discretization: An Enabling Technique. Data Mining and Knowledge Discovery 6:4 (2002) 393-423. Pdf bib
Uniform Frequency Discretizer UniformFrequency-D H. Liu, F. Hussain, L. Tan, M. Dash. Discretization: An Enabling Technique. Data Mining and Knowledge Discovery 6:4 (2002) 393-423. Pdf bib
Fayyad Discretizer Fayyad-D U.M. Fayyad, K.B. Irani. Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning. 13th International Joint Conference on Uncertainly in Artificial Intelligence (IJCAI93). Chambery (France, 1993) 1022-1029. Pdf bib
Iterative Dicotomizer 3 Discretizer ID3-D J.R. Quinlan. Induction of Decision Trees. Machine Learning 1 (1986) 81-106. Pdf bib
Bayesian Discretizer Bayesian-D X. Wu. A Bayesian Discretizer for Real-Valued Attributes. The. Computer J. 39:8 (1996) 688-691. Pdf bib
Mantaras Distance-Based Discretizer MantarasDist-D J. Cerquides, R. López de Màntaras. Proposal and Empirical Comparison of a Parallelizable Distance-Based Discretization Method. 3rd International Conference on Knowledge Discovery and Data Mining (KDD99). NewPort Beach (USA, 1999) 139-142. Pdf bib
Unparametrized Supervised Discretizer USD-D R. Giráldez, J.S. Aguilar-Ruiz, J.C. Riquelme, F. Ferrer-Troyano, D. Rodríguez. Discretization Oriented to Decision Rules Generation. In: L.C. Jain, E. Damiani, R.J. Howlett, N. Ichalkaranje (Eds.) Frontiers in Artificial Intelligence and Applications 82, 2002, 275-279. Pdf bib
R. Giráldez, J.S. Aguilar-Ruiz, J.C. Riquelme. Discretizacion Supervisada no Paramétrica Orientada a la Obtencion de Reglas de Decision.. IX Conferencia de la Asociación Española de Inteligencia Artificial (CAEPIA'01). Gijón (España, 2001) 53-62. Pdf bib
Chi-Merge Discretizer ChiMerge-D R. Kerber. ChiMerge: Discretization of Numeric Attributes. National Conference on Artifical Intelligence American Association for Artificial Intelligence (AAAI'92). San José (California USA, 1992) 123-128. Pdf bib
Chi2 Discretizer Chi2-D H. Liu, R. Setiono. Feature Selection via Discretization. IEEE Transactions on Knowledge and Data Engineering 9:4 (1997) 642-645. Pdf bib
Ameva Discretizer Ameva-D L. Gonzalez-Abril, F.J. Cuberos, F. Velasco, J.A. Ortega. Ameva: An autonomous discretization algorithm. Expert Systems with Applications 36 (2009) 5327-5332. Pdf bib
Zeta Discretizer Zeta-D K.M. Ho, P.D. Scott. Zeta: A Global Method for Discretization of Cotitinuous Variables. 3rd International Conference on Knowledge Discovery and Data Mining (KDD99). NewPort Beach (USA, 1999) 191-194. Pdf bib
Class-Atribute Dependent Discretizer CADD-D J.Y. Ching, A.K.C. Wong, K.C.C. Chan. Class-Dependent Discretization for Inductive Learning from Continuous and Mixed-Mode Data. IEEE Transactions on Pattern Analysis and Machine Intelligence 17:7 (1995) 641-651. Pdf bib
Class-Atribute Interdependence Maximization CAIM-D L.A. Kurgan, K.J. Cios. CAIM Discretization Algorithm. IEEE Transactions on Knowledge and Data Engineering 16:2 (2004) 145-153. Pdf bib
Extended Chi2 Discretizer ExtendedChi2-D C.-T. Sun, J.H. Hsu. An Extended Chi2 Algorithm for Discretization of Real Value Attributes. IEEE Transactions on Knowledge and Data Engineering 17:3 (2005) 437-441. Pdf bib
Fixed Frequency Discretizer FixedFrequency-D Y. Yang, G.I. Webb. Discretization for naive-Bayes learning: managing discretization bias and variance. Machine Learning 74 (2009) 39-74. Pdf bib
Khiops Discretizer Khiops-D M. Boulle. Khiops: A Statistical Discretization Method of Continuous Attributes. Machine Learning 55:1 (2004) 53-69. Pdf bib
Modified Chi2 Discretizer ModifiedChi2-D F.E.H. Tay, L. Shen. A Modified Chi2 Algorithm for Discretization. IEEE Transactions on Knowledge and Data Engineering 14:2 (2002) 666-670. Pdf bib
MODL Discretizer MODL-D M. Boulle. MODL: A bayes optimal discretization method for continuous attributes. Machine Learning 65:1 (2006) 131-165. Pdf bib
1R Discretizer 1R-D R.C. Holte. Very simple classification rules perform well on most commonly used datasets. Machine Learning 11 (1993) 63-91. Pdf bib
Proportional Discretizer Proportional-D Y. Yang, G.I. Webb. Discretization for naive-Bayes learning: managing discretization bias and variance. Machine Learning 74 (2009) 39-74. Pdf bib
Discretization Algorithm Based on a Heterogeneity Criterion HeterDisc-D X. Liu. A Discretization Algorithm Based on a Heterogeneity Criterion. IEEE Transactions on Knowledge and Data Engineering 17:9 (2005) 1166-1173. Pdf bib
Hellinger-based Discretizer HellingerBD-D C. Lee. A Hellinger-based discretization method for numeric attributes in classification learning. Knowledge-Based Systems 20:4 (2007) 419-425. Pdf bib
Distribution-Index-Based Discretizer DIBD-D Q.X. Wu, D.A. Bell, G. Prasad, T.M. McGinnity. A Distribution-Index-Based Discretizer for Decision-Making with Symbolic AI Approaches. IEEE Transactions on Knowledge and Data Engineering 19:1 (2007) 17-28. Pdf bib
Unsupervised Correlation Preserving Discretization UCPD-D S. Mehta, S. Parthasarathy, H. Yang. Toward Unsupervised Correlation Preserving Discretization. IEEE Transactions on Knowledge and Data Engineering 17:9 (2005) 1174-1185. Pdf bib
Interval Distance-Based Method for Discretization IDD-D F.J. Ruiz, C. Angulo, N. Agell. IDD: A Supervised Interval Distance-Based Method for Discretization. IEEE Transactions on Knowledge and Data Engineering 20:9 (2008) 1230-1238. Pdf bib
Discretization algorithm based on Class-Attribute Contingency Coefficient CACC-D C.J. Tsai, C.-I. Lee, W.-P. Yang. A discretization algorithm based on Class-Attribute Contingency Coefficient. Information Sciences 178:3 (2008) 714-731. Pdf bib
Hypercube Division-Based HDD-D P. Yang, J.-S. Li, Y.-X. Huang. HDD: a hypercube division-based algorithm for discretisation. International Journal of Systems Science 42:4 (2011) 557-566. Pdf bib
Cluster Analysis ClusterAnalysis-D M.R. Chmielewski, J.W. Grzymala-Busse. Global discretization of continuous attributes as preprocessing for Machine Learning. International Journal of Approximate Reasoning 15 (1996) 319-331. Pdf bib
Multivariate Discretization MVD-D S.D. Bay. Multivariate Discretization for Set Mining. Knowledge and Information Systems 3 (2001) 491-512. Pdf bib
FUSINTER FUSINTER-D D.A. Zighed, R. Rabaseda, R. Rakotomalala. FUSINTER: A method for discretization of continuous attributes. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 6:3 (1998) 307-326. Pdf bib
Main FEATURE SELECTION
  Full Name    Short Name    Reference  
Mutual Information Feature Selection MIFS-FS R. Battiti. Using Mutual Information For Selection Features In Supervised Neural Net Learning. IEEE Transactions on Neural Networks 5:4 (1994) 537-550. Pdf bib
Las Vegas Filter LVF-FS H. Liu, R. Setiono. A Probabilistic Approach to Feature Selection: A Filter Solution. 13th International Conference on Machine Learning (ICML96 ). Bari (Italy, 1996) 319-327. Pdf bib
H. Liu, H. Motoda. Feature Selection for Knowledge Discovery and Data Mining. Kluwer Academic Publishers, 1998.  bib
FOCUS Focus-FS H. Almuallim, T. Dietterich. Learning With Many Irrelevant Features. 9th National Conference on Artificial Intelligence (AAAI'91). Anaheim (California USA, 1991) 547-552. Pdf bib
Relief Relief-FS K. Kira, L. Rendell. A Practical Approach to Feature Selection. 9th International Workshop on Machine Learning (ML'92). Aberdeen (Scotlant UK, 1992) 249-256. Pdf bib
Las Vegas Wrapper LVW-FS H. Liu, R. Setiono. Feature Selection and Classification: A Probabilistic Wrapper Approach. 9th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems (IEA-AIE'96). Fukuoka (Japon, 1996) 419-424. Pdf bib
H. Liu, H. Motoda. Feature Selection for Knowledge Discovery and Data Mining. Kluwer Academic Publishers, 1998.  bib
Automatic Branch and Bound using Inconsistent Examples Pairs Measure ABB-IEP-FS H. Liu, L. Yu. Toward Integrating Feature Selection Algorithms for Classification and Clustering. IEEE Transactions on Knowledge and Data Engineering 17:4 (2005) 491-502. Pdf bib
H. Liu, H. Motoda. Feature Selection for Knowledge Discovery and Data Mining. Kluwer Academic Publishers, 1998.  bib
Automatic Branch and Bound using Inconsistent Examples Measure ABB-LIU-FS H. Liu, L. Yu. Toward Integrating Feature Selection Algorithms for Classification and Clustering. IEEE Transactions on Knowledge and Data Engineering 17:4 (2005) 491-502. Pdf bib
H. Liu, H. Motoda. Feature Selection for Knowledge Discovery and Data Mining. Kluwer Academic Publishers, 1998.  bib
Automatic Branch and Bound using Mutual Information Measure ABB-MI-FS H. Liu, L. Yu. Toward Integrating Feature Selection Algorithms for Classification and Clustering. IEEE Transactions on Knowledge and Data Engineering 17:4 (2005) 491-502. Pdf bib
H. Liu, H. Motoda. Feature Selection for Knowledge Discovery and Data Mining. Kluwer Academic Publishers, 1998.  bib
Full Exploration using Inconsistent Examples Pairs Measure Full-IEP-FS H. Liu, L. Yu. Toward Integrating Feature Selection Algorithms for Classification and Clustering. IEEE Transactions on Knowledge and Data Engineering 17:4 (2005) 491-502. Pdf bib
H. Liu, H. Motoda. Feature Selection for Knowledge Discovery and Data Mining. Kluwer Academic Publishers, 1998.  bib
Full Exploration (LIU) Full-LIU-FS H. Liu, L. Yu. Toward Integrating Feature Selection Algorithms for Classification and Clustering. IEEE Transactions on Knowledge and Data Engineering 17:4 (2005) 491-502. Pdf bib
H. Liu, H. Motoda. Feature Selection for Knowledge Discovery and Data Mining. Kluwer Academic Publishers, 1998.  bib
Full Exploration using Mutual Information measure Full-MI-FS H. Liu, L. Yu. Toward Integrating Feature Selection Algorithms for Classification and Clustering. IEEE Transactions on Knowledge and Data Engineering 17:4 (2005) 491-502. Pdf bib
H. Liu, H. Motoda. Feature Selection for Knowledge Discovery and Data Mining. Kluwer Academic Publishers, 1998.  bib
Relief-F Relief-F-FS I. Kononenko. Estimating Attributes: Analysis and Extensions of RELIEF. European Conference on Machine Learning 1994 (ECML94). Catania (Italy, 1994) 171-182. Pdf bib
Las Vegas Filter using Inconsistent Examples Pairs Measure LVF-IEP-FS H. Liu, L. Yu. Toward Integrating Feature Selection Algorithms for Classification and Clustering. IEEE Transactions on Knowledge and Data Engineering 17:4 (2005) 491-502. Pdf bib
H. Liu, H. Motoda. Feature Selection for Knowledge Discovery and Data Mining. Kluwer Academic Publishers, 1998.  bib
Simulated Annealing using Inconsistent Examples Pairs measure SA-IEP-FS H. Liu, L. Yu. Toward Integrating Feature Selection Algorithms for Classification and Clustering. IEEE Transactions on Knowledge and Data Engineering 17:4 (2005) 491-502. Pdf bib
H. Liu, H. Motoda. Feature Selection for Knowledge Discovery and Data Mining. Kluwer Academic Publishers, 1998.  bib
Simulated Annealing using Inconsistent Examples measure SA-LIU-FS H. Liu, L. Yu. Toward Integrating Feature Selection Algorithms for Classification and Clustering. IEEE Transactions on Knowledge and Data Engineering 17:4 (2005) 491-502. Pdf bib
H. Liu, H. Motoda. Feature Selection for Knowledge Discovery and Data Mining. Kluwer Academic Publishers, 1998.  bib
Simulated Annealing using Mutual Information measure SA-MI-FS H. Liu, L. Yu. Toward Integrating Feature Selection Algorithms for Classification and Clustering. IEEE Transactions on Knowledge and Data Engineering 17:4 (2005) 491-502. Pdf bib
H. Liu, H. Motoda. Feature Selection for Knowledge Discovery and Data Mining. Kluwer Academic Publishers, 1998.  bib
Sequential Backward Search using Inconsistent Examples Pairs measure SBS-IEP-FS H. Liu, L. Yu. Toward Integrating Feature Selection Algorithms for Classification and Clustering. IEEE Transactions on Knowledge and Data Engineering 17:4 (2005) 491-502. Pdf bib
H. Liu, H. Motoda. Feature Selection for Knowledge Discovery and Data Mining. Kluwer Academic Publishers, 1998.  bib
Sequential Backward Search using Inconsistent Examples measure SBS-LIU-FS H. Liu, L. Yu. Toward Integrating Feature Selection Algorithms for Classification and Clustering. IEEE Transactions on Knowledge and Data Engineering 17:4 (2005) 491-502. Pdf bib
H. Liu, H. Motoda. Feature Selection for Knowledge Discovery and Data Mining. Kluwer Academic Publishers, 1998.  bib
Sequential Backward Search using Mutual Information measure SBS-MI-FS H. Liu, L. Yu. Toward Integrating Feature Selection Algorithms for Classification and Clustering. IEEE Transactions on Knowledge and Data Engineering 17:4 (2005) 491-502. Pdf bib
H. Liu, H. Motoda. Feature Selection for Knowledge Discovery and Data Mining. Kluwer Academic Publishers, 1998.  bib
Sequential Forward Search using Inconsistent Examples Pairs measure SFS-IEP-FS H. Liu, L. Yu. Toward Integrating Feature Selection Algorithms for Classification and Clustering. IEEE Transactions on Knowledge and Data Engineering 17:4 (2005) 491-502. Pdf bib
H. Liu, H. Motoda. Feature Selection for Knowledge Discovery and Data Mining. Kluwer Academic Publishers, 1998.  bib
Sequential Forward Search using Inconsistent Examples measure SFS-LIU-FS H. Liu, L. Yu. Toward Integrating Feature Selection Algorithms for Classification and Clustering. IEEE Transactions on Knowledge and Data Engineering 17:4 (2005) 491-502. Pdf bib
H. Liu, H. Motoda. Feature Selection for Knowledge Discovery and Data Mining. Kluwer Academic Publishers, 1998.  bib
Sequential Forward Search using Mutual Information measure SFS-MI-FS H. Liu, L. Yu. Toward Integrating Feature Selection Algorithms for Classification and Clustering. IEEE Transactions on Knowledge and Data Engineering 17:4 (2005) 491-502. Pdf bib
H. Liu, H. Motoda. Feature Selection for Knowledge Discovery and Data Mining. Kluwer Academic Publishers, 1998.  bib
Main EVOLUTIONARY FEATURE SELECTION
  Full Name    Short Name    Reference  
Steady-state GA with integer coding scheme for wrapper feature selection with k-nn SSGA-Integer-knn-FS J. Casillas, O. Cordón, M.J. del Jesus, F. Herrera. Genetic Feature Selection in a Fuzzy Rule-Based Classification System Learning Process. Information Sciences 136:1-4 (2001) 135-157. Pdf bib
Generational GA with binary coding scheme for filter feature selection with the inconsistency rate GGA-Binary-Inconsistency-FS P.L. Lanzi. Fast Feature Selection With Genetic Algorithms: A Filter Approach. IEEE International Conference on Evolutionary Computation. Indianapolis. Indianapolis (USA, 1997) 537-540. Pdf bib
Generational Genetic Algorithm for Feature Selection GGA-FS J. Yang, V. Honavar. Feature Subset Selection Using a Genetic Algorithm. IEEE Intelligent Systems 13:2 (1998) 44-49. Pdf bib
Main TRAINING SET SELECTION
  Full Name    Short Name    Reference  
Pattern by Ordered Projections POP-TSS J.C. Riquelme, J.S. Aguilar-Ruiz, M. Toro. Finding representative patterns with ordered projections. Pattern Recognition 36 (2003) 1009-1018. Pdf bib
Prototipe Selection by Relative Certainty Gain PSRCG-TSS M. Sebban, R. Nock, S. Lallich. Stopping Criterion for Boosting-Based Data Reduction Techniques: from Binary to Multiclass Problems. Journal of Machine Learning Research 3 (2002) 863-885. Pdf bib
Variable Similarity Metric VSM-TSS D.G. Lowe. Similarity Metric Learning For A Variable-Kernel Classifier. Neural Computation 7:1 (1995) 72-85. Pdf bib
Edited Nearest Neighbor ENN-TSS D.L. Wilson. Asymptotic Properties Of Nearest Neighbor Rules Using Edited Data. IEEE Transactions on Systems, Man and Cybernetics 2:3 (1972) 408-421. Pdf bib
Multiedit Multiedit-TSS P.A. Devijver. On the editing rate of the MULTIEDIT algorithm. Pattern Recognition Letters 4:1 (1986) 9-12. Pdf bib
Prototipe Selection based on Relative Neighbourhood Graphs RNG-TSS J.S. Sánchez, F. Pla, F.J. Ferri. Prototype selection for the nearest neighbor rule through proximity graphs. Pattern Recognition Letters 18 (1997) 507-513. Pdf bib
Modified Edited Nearest Neighbor MENN-TSS K. Hattori, M. Takahashi. A new edited k-nearest neighbor rule in the pattern classification problem. Pattern Recognition 33 (2000) 521-528. Pdf bib
Nearest Centroid Neighbourhood Edition NCNEdit-TSS J.S. Sánchez, R. Barandela, A.I. Márques, R. Alejo, J. Badenas. Analysis of new techniques to obtain quality training sets. Pattern Recognition Letters 24 (2003) 1015-1022. Pdf bib
Edited NRBF ENRBF-TSS M. Grochowski, N. Jankowski. Comparison of instance selection algorithms I. Algorithms survey. VII International Conference on Artificial Intelligence and Soft Computing (ICAISC'04). LNCS 3070, Springer 2004, Zakopane (Poland, 2004) 598-603. Pdf bib
Edited Nearest Neighbor with Estimation of Probabilities Threshold ENNTh-TSS F. Vazquez, J.S. Sánchez, F. Pla. A stochastic approach to Wilson's editing algorithm. 2nd Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA05). LNCS 3523, Springer 2005, Estoril (Portugal, 2005) 35-42. Pdf bib
All-KNN AllKNN-TSS I. Tomek. An Experiment With The Edited Nearest-Neighbor Rule. IEEE Transactions on Systems, Man and Cybernetics 6:6 (1976) 448-452. Pdf bib
Model Class Selection ModelCS-TSS C.E. Brodley. Adressing The Selective Superiority Problem: Automatic Algorithm/Model Class Selection. 10th International Machine Learning Conference (ICML'93). Amherst (MA USA, 1993) 17-24. Pdf bib
Main EVOLUTIONARY TRAINING SET SELECTION
  Full Name    Short Name    Reference  
CHC Adaptative Search for Instance Selection CHC-TSS J.R. Cano, F. Herrera, M. Lozano. Using Evolutionary Algorithms As Instance Selection For Data Reduction In KDD: An Experimental Study. IEEE Transactions on Evolutionary Computation 7:6 (2003) 561-575. Pdf bib
Generational Genetic Algorithm for Instance Selection GGA-TSS J.R. Cano, F. Herrera, M. Lozano. Using Evolutionary Algorithms As Instance Selection For Data Reduction In KDD: An Experimental Study. IEEE Transactions on Evolutionary Computation 7:6 (2003) 561-575. Pdf bib
Steady-State Genetic Algorithm for Instance Selection SGA-TSS J.R. Cano, F. Herrera, M. Lozano. Using Evolutionary Algorithms As Instance Selection For Data Reduction In KDD: An Experimental Study. IEEE Transactions on Evolutionary Computation 7:6 (2003) 561-575. Pdf bib
Population-Based Incremental Learning PBIL-TSS J.R. Cano, F. Herrera, M. Lozano. Using Evolutionary Algorithms As Instance Selection For Data Reduction In KDD: An Experimental Study. IEEE Transactions on Evolutionary Computation 7:6 (2003) 561-575. Pdf bib
Main MISSING VALUES
  Full Name    Short Name    Reference  
Delete Instances with Missing Values Ignore-MV P.A. Gourraud, E. Ginin, A. Cambon-Thomsen. Handling Missing Values In Population Data: Consequences For Maximum Likelihood Estimation Of Haplotype Frequencies. European Journal of Human Genetics 12:10 (2004) 805-812. Pdf bib
Event Covering Synthesizing EventCovering-MV D.K.Y. Chiu, A.K.C. Wong. Synthesizing Knowledge: A Cluster Analysis Approach Using Event-Covering. IEEE Transactions on Systems, Man and Cybernetics, Part B 16:2 (1986) 251-259. Pdf bib
K-Nearest Neighbor Imputation KNN-MV G.E.A.P.A. Batista, M.C. Monard. An Analysis Of Four Missing Data Treatment Methods For Supervised learning. Applied Artificial Intelligence 17:5 (2003) 519-533. Pdf bib
Most Common Attribute Value MostCommon-MV J.W. Grzymala-Busse, L.K. Goodwin, W.J. Grzymala-Busse, X. Zheng. Handling Missing Attribute Values in Preterm Birth Data Sets. 10th International Conference of Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (RSFDGrC'05). LNCS 3642, Springer 2005, Regina (Canada, 2005) 342-351. Pdf bib
Assign All Posible Values of the Attribute AllPossible-MV J.W. Grzymala-Busse. On the Unknown Attribute Values In Learning From Examples. 6th International Symposium on Methodologies For Intelligent Systems (ISMIS91). Charlotte (USA, 1991) 368-377. Pdf bib
K-means Imputation KMeans-MV J. Deogun, W. Spaulding, B. Shuart, D. Li. Towards Missing Data Imputation: A Study of Fuzzy K-means Clustering Method. 4th International Conference of Rough Sets and Current Trends in Computing (RSCTC'04). LNCS 3066, Springer 2004, Uppsala (Sweden, 2004) 573-579. Pdf bib
Concept Most Common Attribute Value ConceptMostCommon-MV J.W. Grzymala-Busse, L.K. Goodwin, W.J. Grzymala-Busse, X. Zheng. Handling Missing Attribute Values in Preterm Birth Data Sets. 10th International Conference of Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (RSFDGrC'05). LNCS 3642, Springer 2005, Regina (Canada, 2005) 342-351. Pdf bib
Assign All Posible Values of the Attribute Restricted to the Given Concept ConceptAllPossible-MV J.W. Grzymala-Busse. On the Unknown Attribute Values In Learning From Examples. 6th International Symposium on Methodologies For Intelligent Systems (ISMIS91). Charlotte (USA, 1991) 368-377. Pdf bib
Fuzzy K-means Imputation FKMeans-MV J. Deogun, W. Spaulding, B. Shuart, D. Li. Towards Missing Data Imputation: A Study of Fuzzy K-means Clustering Method. 4th International Conference of Rough Sets and Current Trends in Computing (RSCTC'04). LNCS 3066, Springer 2004, Uppsala (Sweden, 2004) 573-579. Pdf bib
Support Vector Machine Imputation SVMimpute-MV H.A.B. Feng, G.C. Chen, C.D. Yin, B.B. Yang, Y.E. Chen. A SVM regression based approach to filling in Missing Values. 9th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems (KES2005). LNCS 3683, Springer 2005, Melbourne (Australia, 2005) 581-587. Pdf bib
Weighted K-Nearest Neighbor Imputation WKNNimpute-MV O. Troyanskaya, M. Cantor, G. Sherlock, P. Brown, T. Hastie, R. Tibshirani, D. Botstein, R.B. Altman. Missing value estimation methods for DNA microarrays. Bioinformatics 17 (2001) 520-525. Pdf bib
Bayesian Principal Component Analysis BPCA-MV S. Oba, M. Sato, I. Takemasa, M. Monden, K. Matsubara, S. Ishii. A Bayesian missing value estimation method for gene expression profile data. Bioinformatics 19 (2003) 2088-2096. Pdf bib
Expectation-Maximization single imputation EM-MV T. Schneider. Analysis of incomplete climate data: Estimation of Mean Values and covariance matrices and imputation of Missing values. Journal of Climate 14 (2001) 853-871. Pdf bib
Local Least Squares Imputation LLSImpute-MV H.A. Kim, G.H. Golub, H. Park. Missing value estimation for DNA microarray gene expression data: Local least squares imputation. Bioinformatics 21:2 (2005) 187-198. Pdf bib
Single Vector Decomposition imputation SVDImpute-MV O. Troyanskaya, M. Cantor, G. Sherlock, P. Brown, T. Hastie, R. Tibshirani, D. Botstein, R.B. Altman. Missing value estimation methods for DNA microarrays. Bioinformatics 17 (2001) 520-525. Pdf bib
Main TRANSFORMATION
  Full Name    Short Name    Reference  
Decimal Scaling ranging DecimalScaling-TR L.A. Shalabi, Z. Shaaban, B. Kasasbeh. Data Mining: A Preprocessing Engine. Journal of Computer Science 2:9 (2006) 735-735. Pdf bib
Min Max ranging MinMax-TR L.A. Shalabi, Z. Shaaban, B. Kasasbeh. Data Mining: A Preprocessing Engine. Journal of Computer Science 2:9 (2006) 735-735. Pdf bib
Z Score ranging ZScore-TR L.A. Shalabi, Z. Shaaban, B. Kasasbeh. Data Mining: A Preprocessing Engine. Journal of Computer Science 2:9 (2006) 735-735. Pdf bib
Nominal to Binary transformation Nominal2Binary-TR L.A. Shalabi, Z. Shaaban, B. Kasasbeh. Data Mining: A Preprocessing Engine. Journal of Computer Science 2:9 (2006) 735-735. Pdf bib
Main DATA COMPLEXITY
  Full Name    Short Name    Reference  
Data Complexity Metrics calculation Metrics-DC T.K. Ho, M. Basu. Complexity measures of supervised classification problems. IEEE Transactions on Pattern Analysis and Machine Intelligence 24:3 (2002) 289-300. Pdf bib
Main NOISY DATA FILTERING
  Full Name    Short Name    Reference  
Saturation Filter SaturationFilter-F D. Gamberger, N. Lavrac, S. Dzroski. Noise detection and elimination in data preprocessing: Experiments in medical domains. Applied Artificial Intelligence 14:2 (2000) 205-223. Pdf bib
Pairwise Attribute Noise Detection Algorithm Filter PANDA-F J.D. Hulse, T.M. Khoshgoftaar, H. Huang. The pairwise attribute noise detection algorithm. Knowledge and Information Systems 11:2 (2007) 171-190. Pdf bib
Classification Filter ClassificationFilter-F D. Gamberger, N. Lavrac, C. Groselj. Experiments with noise filtering in a medical domain. 16th International Conference on Machine Learning (ICML99). San Francisco (USA, 1999) 143-151. Pdf bib
Automatic Noise Remover ANR-F X. Zeng, T. Martinez. A Noise Filtering Method Using Neural Networks. IEEE International Workshop on Soft Computing Techniques in Instrumentation, Measurement and Related Applications (SCIMA2003). Utah (USA, 2003) 26-31. Pdf bib
Ensemble Filter EnsembleFilter-F C.E. Brodley, M.A. Friedl. Identifying Mislabeled Training Data. Journal of Artificial Intelligence Research 11 (1999) 131-167. Pdf bib
Cross-Validated Committees Filter CVCommitteesFilter-F S. Verbaeten, A.V. Assche. Ensemble methods for noise elimination in classification problems. 4th International Workshop on Multiple Classifier Systems (MCS 2003). LNCS 2709, Springer 2003, Guilford (UK, 2003) 317-325. Pdf bib
Iterative-Partitioning Filter IterativePartitioningFilter-F T.M. Khoshgoftaar, P. Rebours. Improving software quality prediction by noise filtering techniques. Journal of Computer Science and Technology 22 (2007) 387-396. Pdf bib



Main Classification Algorithms

Main CRISP RULE LEARNING FOR CLASSIFICATION
  Full Name    Short Name    Reference  
AQ-15 AQ-C R.S. Michalksi,, I. Mozetic, N. Lavrac. The Multipurpose Incremental Learning System AQ15 And Its Testing Application To Three Medical Domains. 5th INational Conference on Artificial Intelligence (AAAI'86 ). Philadelphia (Pennsylvania, 1986) 1041-1045. Pdf bib
CN2 CN2-C P. Clark, T. Niblett. The CN2 Induction Algorithm. Machine Learning Journal 3:4 (1989) 261-283. Pdf bib
PRISM PRISM-C J. Cendrowska. PRISM: An algorithm for inducing modular rules. International Journal of Man-Machine Studies 27:4 (1987) 349-370. Pdf bib
1R 1R-C R.C. Holte. Very simple classification rules perform well on most commonly used datasets. Machine Learning 11 (1993) 63-91. Pdf bib
Rule Induction with Optimal Neighbourhood Algorithm Riona-C G. Góra, A. Wojna. RIONA: A New Classification System Combining Rule Induction and Instance-Based Learning. Fundamenta Informaticae 51:4 (2002) 1-22. Pdf bib
C4.5Rules C45Rules-C J.R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kauffman Publishers, 1993.  bib
J.R. Quinlan. MDL and Categorical Theories (Continued). Machine Learning: Proceedings of the Twelfth International Conference. Lake Tahoe California (United States of America, 1995) 464-470. Pdf bib
C4.5Rules (Simulated Annealing version) C45RulesSA-C J.R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kauffman Publishers, 1993.  bib
J.R. Quinlan. MDL and Categorical Theories (Continued). Machine Learning: Proceedings of the Twelfth International Conference. Lake Tahoe California (United States of America, 1995) 464-470. Pdf bib
PART PART-C E. Frank, I.H. Witten. Generating Accurate Rule Sets Without Global Optimization. Proceedings of the Fifteenth International Conference on Machine Learning. (1998) 144-151. Pdf bib
Repeated Incremental Pruning to Produce Error Reduction Ripper-C W.W. Cohen. Fast Effective Rule Induction. Machine Learning: Proceedings of the Twelfth International Conference. Lake Tahoe California (United States of America, 1995) 1-10. Pdf bib
Simple Learner with Iterative Pruning to Produce Error Reduction Slipper-C W.W. Cohen, Y. Singer. A Simple, Fast, and Effective Rule Learner. Proceedings of the Sixteenth National Conference on Artificial Intelligence. Orlando Florida (United States of America, 1999) 335-342. Pdf bib
Association Rule Tree ART-C F. Berzal, J.C. Cubero, D. Sánchez, J.M. Serrano. Serrano.ART: A Hybrid Classification Model. Machine Learning 54 (2004) 67-92. Pdf bib
DataSqueezer DataSqueezer-C L.A. Kurgan, K.J. Cios, S. Dick. Highly Scalable and Robust Rule Learner: Performance Evaluation and Comparison. IEEE Transactions on Systems, Man and Cybernetics,Part B: Cybernetics 36:1 (2006) 32-53. Pdf bib
Swap1 Swap1-C M. Sholom, N. Indurkhya. Optimized Rule Induction. IEEE Expert 1 (1993) 61-70. Pdf bib
Learning Examples Module 1 LEM1-C J. Stefanowski. On rough set based approaches to induction of decision rules. In: L. Polkowski, A. Skowron (Eds.) Rough sets in data mining and knowledge discovery, 1998, 500-529. Pdf bib
Learning Examples Module 2 LEM2-C J. Stefanowski. On rough set based approaches to induction of decision rules. In: L. Polkowski, A. Skowron (Eds.) Rough sets in data mining and knowledge discovery, 1998, 500-529. Pdf bib
Rule Induction Two In One Ritio-C X. Wu, D. Urpani. Induction By Attribute Elimination. IEEE Transactions on Knowledge and Data Engineering 11:5 (1999) 805-812. Pdf bib
Rule Extraction System Version 6 Rules6-C D.T. Pham, A.A. Afify. RULES-6: A Simple Rule Induction Algorithm for Supporting Decision Making. 31st Annual Conference of IEEE Industrial Electronics Society (IECON). (2005) 2184-2189. Pdf bib
Scalable Rule Induction SRI-C D.T. Pham, A.A. Afify. SRI: a scalable rule induction algorithm. Proceedings of the Institution of Mechanical Engineers . (2006) 537-552.  bib
Rule-MINI RMini-C S.J. Hong. R-MINI: An Iterative Approach for Generating Minimal Rules from Examples. IEEE Transactions on Knowledge and Data Engineering 9:5 (1997) 709-717. Pdf bib
Main EVOLUTIONARY CRISP RULE LEARNING FOR CLASSIFICATION
  Full Name    Short Name    Reference  
Genetic Algorithm based Classifier System with Adaptive Discretization Intervals GAssist-ADI-C J. Bacardit, J.M. Garrell. Evolving multiple discretizations with adaptive intervals for a pittsburgh rule-based learning classifier system. Genetic and Evolutionary Computation Conference (GECCO'03). LNCS 2724, Springer 2003, Chicago (Illinois USA, 2003) 1818-1831. Pdf bib
J. Bacardit, J.M. Garrell. Analysis and improvements of the adaptive discretization intervals knowledge representatio. Genetic and Evolutionary Computation Conference (GECCO'04). LNCS 3103, Springer 2004, Seattle (Washington USA, 2004) 726-738. Pdf bib
Pittsburgh Genetic Interval Rule Learning Algorithm PGIRLA-C A.L. Corcoran, S. Sen. Using Real-Valued Genetic Algorithms to Evolve Rule Sets for Classification. 1st IEEE Conference on Evolutionary Computation. Orlando (Florida, 1994) 120-124. Pdf bib
Supervised Inductive Algorithm SIA-C G. Venturini. SIA: A Supervised Inductive Algorithm with Genetic Search for Learning Attributes Based Concepts. 6th European Conference on Machine Learning (ECML'93). Lecture Notes in Artificial Intelligence. Viena (Austria, 1993) 280-296. Pdf bib
X-Classifier System XCS-C S.W. Wilson. Classifier Fitness Based on Accuracy. Evolutionary Computation 3:2 (1995) 149-175. Pdf bib
Hierarchical Decision Rules Hider-C J.S. Aguilar-Ruiz, J.C. Riquelme, M. Toro. Evolutionary learning of hierarchical decision rules.. Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics 33:2 (2003) 324-331. Pdf bib
J.S. Aguilar-Ruiz, R. Giráldez, J.C. Riquelme. Natural Encoding for Evolutionary Supervised Learning. IEEE Transactions on Evolutionary Computation 11:4 (2007) 466-479. Pdf bib
Genetic Algorithm based Classifier System with Intervalar Rules GAssist-Intervalar-C J. Bacardit, J.M. Garrell. Bloat control and generalization pressure using the minimum description length principle for a pittsburgh approach learning classifier system. Advances at the frontier of Learning Classifier Systems. Springer Berlin-Heidelberg. (2007) 61-80. Pdf bib
LOgic grammar Based GENetic PROgramming system LogenPro-C M.L. Wong, K.S. Leung. Data Mining using grammar based genetic programming and applications. Kluwer Academics Publishers, 2000.  bib
sUpervised Classifier System UCS-C E. Bernadó-Mansilla, J.M. Garrell. Accuracy-Based Learning Classifier Systems: Models, Analysis and Applications to Classification Tasks. Evolutionary Computation 11:3 (2003) 209-238. Pdf bib
Particle Swarm Optimization / Ant Colony Optimization for Classification PSO_ACO-C T. Sousa, A. Silva, A. Neves. Particle Swarm based Data Mining Algorithms for classification tasks. Parallel Computing 30 (2004) 767-783. Pdf bib
Ant Miner Ant_Miner-C R.S. Parpinelli, H.S. Lopes, A.A. Freitas. Data Mining With an Ant Colony Optimization Algorithm. IEEE Transactions on Evolutionary Computation 6:4 (2002) 321-332. Pdf bib
Advanced Ant Miner Advanced_Ant_Miner-C R.S. Parpinelli, H.S. Lopes, A.A. Freitas. Data Mining With an Ant Colony Optimization Algorithm. IEEE Transactions on Evolutionary Computation 6:4 (2002) 321-332. Pdf bib
R.S. Parpinelli, H.S. Lopes, A.A. Freitas. An Ant Colony Algorithm for Classification Rule Discovery. In: H.A. Abbass, R.A. Sarker, C.S. Newton (Eds.) Data Mining: a Heuristic Approach, 2002, 191-208. Pdf bib
Ant Miner+ Ant_Miner_Plus-C R.S. Parpinelli, H.S. Lopes, A.A. Freitas. Data Mining With an Ant Colony Optimization Algorithm. IEEE Transactions on Evolutionary Computation 6:4 (2002) 321-332. Pdf bib
Advanced Ant Miner+ Advanced_Ant_Miner_Plus-C R.S. Parpinelli, H.S. Lopes, A.A. Freitas. Data Mining With an Ant Colony Optimization Algorithm. IEEE Transactions on Evolutionary Computation 6:4 (2002) 321-332. Pdf bib
R.S. Parpinelli, H.S. Lopes, A.A. Freitas. An Ant Colony Algorithm for Classification Rule Discovery. In: H.A. Abbass, R.A. Sarker, C.S. Newton (Eds.) Data Mining: a Heuristic Approach, 2002, 191-208. Pdf bib
Constricted Particle Swarm Optimization CPSO-C T. Sousa, A. Silva, A. Neves. Particle Swarm based Data Mining Algorithms for classification tasks. Parallel Computing 30 (2004) 767-783. Pdf bib
Linear Decreasing Weight - Particle Swarm Optimization LDWPSO-C T. Sousa, A. Silva, A. Neves. Particle Swarm based Data Mining Algorithms for classification tasks. Parallel Computing 30 (2004) 767-783. Pdf bib
Real Encoding - Particle Swarm Optimization REPSO-C Y. Liu, Z. Qin, Z. Shi, J. Chen. Rule Discovery with Particle Swarm Optimization. Advanced Workshop on Content Computing (AWCC). LNCS 3309, Springer 2004 (2004) 291-296. Pdf bib
Bioinformatics-oriented hierarchical evolutionary learning BioHel-C J. Bacardit, E. Burke, N. Krasnogor. Improving the scalability of rule-based evolutionary learning. Memetic computing 1:1 (2009) 55-67. Pdf bib
COverage-based Genetic INduction COGIN-C D.P. Greene, S.F. Smith. Competition–based induction of decision models from examples. Machine Learning 13:23 (1993) 229-257. Pdf bib
CO-Evolutionary Rule Extractor CORE-C K.C. Tan, Q. Yu, J.H. Ang. A coevolutionary algorithm for rules discovery in data mining. International Journal of Systems Science 37:12 (2006) 835-864. Pdf bib
Data Mining for Evolutionary Learning DMEL-C W.H. Au, K.C.C. Chan, X. Yao. A novel evolutionary data mining algorithm with applications to churn prediction. IEEE Transactions on Evolutionary Computation 7:6 (2003) 532-545. Pdf bib
Genetic-based Inductive Learning GIL-C C.Z. Janikow. A knowledge–intensive genetic algorithm for supervised learning. Machine Learning 13:2 (1993) 189-228. Pdf bib
Organizational Co-Evolutionary algorithm for Classification OCEC-C L. Jiao, J. Liu, W. Zhong. An organizational coevolutionary algorithm for classification. IEEE Transactions on Evolutionary Computation 10:12 (2006) 67-80. Pdf bib
Ordered Incremental Genetic Algorithm OIGA-C F. Zhu, S.U. Guan. Ordered incremental training with genetic algorithms. International Journal of Intelligent Systems 19:12 (2004) 1239-1256. Pdf bib
Incremental Learning with Genetic Algorithms ILGA-C S.U. Guan, F. Zhu. An incremental approach to genetic– algorithms–based classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B 35:2 (2005) 227-239. Pdf bib
Memetic Pittsburgh Learning Classifier System MPLCS-C J. Bacardit, N. Krasnogor. Performance and Efficiency of Memetic Pittsburgh Learning Classifier Systems. Evolutionary Computation 17:3 (2009) 307-342. Pdf bib
Bojarczuk Genetic programming method Bojarczuk_GP-C C.C. Bojarczuk, H.S. Lopes, A.A. Freitas, E.L. Michalkiewicz. A constrained-syntax genetic programming system for discovering classification rules: applications to medical datasets. Artificial Intelligence in Medicine 30:1 (2004) 27-48. Pdf bib
Falco Genetic programming method Falco_GP-C I.D. Falco, A.D. Cioppa, E. Tarantino. Discovering interesting classification rules with genetic programming. Applied Soft Computing 1 (2002) 257-269. Pdf bib
Tan Genetic programming method Tan_GP-C K.C. Tan, A. Tay, T.H. Lee, C.M. Heng. Mining multiple comprehensible classification rules using genetic programming. he 2002 Congress on Evolutionary Computation (CEC02). Piscataway (USA, 2002) 1302-1307. Pdf bib
Genetic Algorithm designed for the task of solving problem MAX-F Olex-GA-C A. Pietramala, V.L. Policicchio, P. Rullo, I. Sidhu. A Genetic Algorithm for Text Classification Rule Induction. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2008). LNCS 5212, Springer 2008, Antwerp (Belgium, 2008) 188-203. Pdf bib
Main FUZZY RULE LEARNING FOR CLASSIFICATION
  Full Name    Short Name    Reference  
Fuzzy Rule Learning Model by the Chi et al. approach with rule weights Chi-RW-C Z. Chi, H. Yan, T. Pham. Fuzzy Algorithms: With Applications To Image Processing and Pattern Recognition. World Scientific, 1996.  bib
O. Cordón, M.J. del Jesus, F. Herrera. A proposal on reasoning methods in fuzzy rule-based classification systems. International Journal of Approximate 20:1 (1999) 21-45. Pdf bib
H. Ishibuchi, T. Yamamoto. Rule weight specification in fuzzy rule-based classification systems. IEEE Transactions on Fuzzy Systems 13:4 (2005) 428-435. Pdf bib
Weighted Fuzzy Classifier WF-C T. Nakashima, G. Schaefer, Y. Yokota, H. Ishibuchi. A Weighted Fuzzy Classifier and its Application to Image Processing Tasks. Fuzzy Sets and Systems 158 (2007) 284-294. Pdf bib
Positive Definite Fuzzy Classifier PDFC-C Y. Chen, J.Z. Wang. Support Vector Learning for Fuzzy Rule-Based Classification Systems. IEEE Transactions on Fuzzy Systems 11:6 (2003) 716-728. Pdf bib
Fuzzy Unordered Rule Induction Algorithm FURIA-C J. Hühn, E. Hüllermeier. FURIA: an algorithm for unordered fuzzy rule induction. Data Mining and Knowledge Discovery 19:3 (2009) 293-319. Pdf bib
Main EVOLUTIONARY FUZZY RULE LEARNING FOR CLASSIFICATION
  Full Name    Short Name    Reference  
Fuzzy Learning based on Genetic Programming Grammar Operators and Simulated Annealing GFS-SP-C L. Sánchez, I. Couso, J.A. Corrales. Combining GP Operators With SA Search To Evolve Fuzzy Rule Based Classifiers. Information Sciences 136:1-4 (2001) 175-192. Pdf bib
Fuzzy Learning based on Genetic Programming Grammar Operators GFS-GPG-C L. Sánchez, I. Couso, J.A. Corrales. Combining GP Operators With SA Search To Evolve Fuzzy Rule Based Classifiers. Information Sciences 136:1-4 (2001) 175-192. Pdf bib
Fuzzy AdaBoost GFS-AdaBoost-C M.J. del Jesus, F. Hoffmann, L. Junco, L. Sánchez. Induction of Fuzzy-Rule-Based Classifiers With Evolutionary Boosting Algorithms. IEEE Transactions on Fuzzy Systems 12:3 (2004) 296-308. Pdf bib
LogitBoost GFS-LogitBoost-C J. Otero, L. Sánchez. Induction of Descriptive Fuzzy Classifiers With The Logitboost Algorithm. Soft Computing 10:9 (2006) 825-835. Pdf bib
Fuzzy Learning based on Genetic Programming GFS-GP-C L. Sánchez, I. Couso, J.A. Corrales. Combining GP Operators With SA Search To Evolve Fuzzy Rule Based Classifiers. Information Sciences 136:1-4 (2001) 175-192. Pdf bib
Logitboost with Single Winner Inference GFS-MaxLogitBoost-C L. Sánchez, J. Otero. Boosting Fuzzy Rules in Classification Problems Under Single-Winner Inference. International Journal of Intelligent Systems 22:9 (2007) 1021-1034. Pdf bib
Grid Rule Base Generation and Genetic Rule Selection GFS-Selec-C H. Ishibuchi, K. Nozaki, N. Yamamoto, H. Tanaka. Selecting Fuzzy If-Then Rules for Classification. IEEE Transactions on Fuzzy Systems 3:3 (1995) 260-270. Pdf bib
Structural Learning Algorithm in a Vague Environment with Feature Selection SLAVE-C A. González, R. Perez. Selection of relevant features in a fuzzy genetic learning algorithm. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 31:3 (2001) 417-425. Pdf bib
Methodology to Obtain Genetic fuzzy rule-based systems Under the iterative Learning approach MOGUL-C O. Cordón, M.J. del Jesus, F. Herrera. Genetic learning of fuzzy rule-based classification systems cooperating with fuzzy reasoning methods. International Journal of Intelligent Systems 13:10 (1998) 1025-1053. Pdf bib
O. Cordón, M.J. del Jesus, F. Herrera, M. Lozano. MOGUL: A Methodology to Obtain Genetic fuzzy rule-based systems Under the iterative rule Learning approach. International Journal of Intelligent Systems 14:11 (1999) 1123-1153. Pdf bib
Fuzzy rule approach based on a genetic cooperative-competitive learning GFS-GCCL-C H. Ishibuchi, T. Nakashima, T. Murata. Performance evaluation of fuzzy classifier systems for multidimensional pattern classification problems. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 29:5 (1999) 601-618. Pdf bib
Fuzzy Hybrid Genetics-Based Machine Learning FH-GBML-C H. Ishibuchi, T. Yamamoto, T. Nakashima. Hybridization of Fuzzy GBML Approaches for Pattern Classification Problems. IEEE Transactions on Systems, Man and Cybernetics - Part B: Cybernetics 35:2 (2005) 359-365. Pdf bib
Fuzzy Expert System GFS-ES-C Y. Shi, R. Eberhart, Y. Chen. Implementation of evolutionary fuzzy systems. IEEE Transactions on Fuzzy Systems 7:2 (1999) 109-119. Pdf bib
Steady-State Genetic Algorithm for Extracting Fuzzy Classification Rules From Data SGERD-C E.G. Mansoori, M.J. Zolghadri, S.D. Katebi. SGERD: A Steady-State Genetic Algorithm for Extracting Fuzzy Classification Rules From Data. IEEE Transactions on Fuzzy Systems 16:4 (2008) 1061-1071. Pdf bib
Genetic Programming-based learning of COmpact and ACcurate fuzzy rule-based classification systems for High-dimensional problems GP-COACH-C F.J. Berlanga, A.J. Rivera, M.J. del Jesus, F. Herrera. GP-COACH: Genetic Programming based learning of COmpact and ACcurate fuzzy rule based classification systems for high dimensional problems. Information Sciences 180:8 (2010) 1183-1200. Pdf bib
Linguistic fuzzy rule-based classification Interval-Valued fuzzy reasoning method with TUning and Rule Selection IVTURS-C J. Sanz, A. Fernández, H. Bustince, F. Herrera. IVTURS: a linguistic fuzzy rule-based classification system based on a new Interval-Valued fuzzy reasoning method with TUning and Rule Selection. IEEE Transactions on Fuzzy Systems 21:3 (2013) 399-411. Pdf bib
Genetic Programming-based learning of COmpact and ACcurate fuzzy rule-based classification systems for High-dimensional problems GP-COACH F.J. Berlanga, A.J. Rivera, M.J. del Jesus, F. Herrera. GP-COACH: Genetic Programming based learning of COmpact and ACcurate fuzzy rule based classification systems for high dimensional problems. Information Sciences 180:8 (2010) 1183-1200. Pdf bib
New SLAVE NSLV-C A. González, R. Perez. Improving the genetic algorithm of SLAVE. Mathware and Soft Computing 16 (2009) 59-70. Pdf bib
D. Garcia, A. González, R. Perez. Overview of the SLAVE learning algorithm: A review of its evolution and prospects. International Journal of Computational Intelligence Systems 7:6 (2014) 0-1221. Pdf bib
SLAVE2 SLAVE2-C A. González, R. Perez. SLAVE: A genetic learning system based on an iterative approach. IEEE Trans. on Fuzzy Systems 7:2 (1999) 176-191. Pdf bib
A. González, R. Perez. Selection of relevant features in a fuzzy genetic learning algorithm. IEEE Trans. on Systems, Man and Cybernetics-Part. B Cy-bernetics 31:3 (2001) 0-425. Pdf bib
SLAVEv0: Structural Learning Algorithm in a Vague Environment SLAVEv0-C A. González, R. Perez. Completeness and consistency conditions for learning fuzzy rules. Fuzzy Sets and Systems 96 (1998) 37-51. Pdf bib
D. Garcia, A. González, R. Perez. Overview of the SLAVE learning algorithm: A review of its evolution and prospects. International Journal of Computational Intelligence Systems 7:6 (2014) 0-1221. Pdf bib
Main NESTED GENERALIZED LEARNING
  Full Name    Short Name    Reference  
Batch Nested Generalized Exemplar BNGE-C D. Wettschereck, T.G. Dietterich. An Experimental Comparison of the Nearest-Neighbor and Nearest-Hyperrectangle Algorithms. Machine Learning 19 (1995) 5-27. Pdf bib
Exemplar-Aided Constructor of Hyperrectangles EACH-C S. Salzberg. A Nearest Hyperrectangle Learning Method. Machine Learning 6 (1991) 251-276. Pdf bib
INNER INNER-C O. Luaces. Inflating examples to obtain rules. International Journal of Intelligent Systems 18 (2003) 1113-1143. Pdf bib
Rule Induction from a Set of Exemplars RISE-C P. Domingos. Unifying Instance-Based and Rule-Based Induction. Machine Learning 24:2 (1996) 141-168. Pdf bib
Evolutionary Hyperrectangle Selection based on CHC EHS_CHC-C S. García, J. Derrac, J. Luengo, C.J. Carmona, F. Herrera. Evolutionary Selection of Hyperrectangles in Nested Generalized Exemplar Learning. Applied Soft Computing 11:3 (2011) 3032-3045. Pdf bib
Main ASSOCIATIVE CLASSIFICATION
  Full Name    Short Name    Reference  
Classification Based on Associations CBA-C B. Liu, W. Hsu, Y. Ma. Integrating Classification and Association Rule Mining. 4th International Conference on Knowledge Discovery and Data Mining (KDD98). New York (USA, 1998) 80-86. Pdf bib
Classification Based on Associations 2 CBA2-C B. Liu, Y. Ma, C.K. Wong. Classification Using Association Rules: Weaknesses and Enhancements . In: R.L. Grossman, C. Kamath, V. Kumar (Eds.) Data Mining for Scientific and Engineering Applications, 2001, 591-601. Pdf bib
Classification based on Predictive Association Rules CPAR-C X. Yin, J. Han. CPAR: Classification based on Predictive Association Rules. 3rd SIAM International Conference on Data Mining (SDM03). San Francisco (USA, 2003) 331-335. Pdf bib
Classification Based on Multiple Class-Association Rules CMAR-C W. Li, J. Han, J. Pei. CMAR: Accurate and efficient classification based on multiple class-association rules. 2001 IEEE International Conference on Data Mining (ICDM01). San Jose (USA, 2001) 369-376. Pdf bib
Fuzzy rules for classification problems based on the Apriori algorithm FCRA-C Y.-C. Hu, R.-S. Chen, G.-H. Tzeng. Finding fuzzy classification rules using data mining techniques. Pattern Recognition Letters 24:1-3 (2003) 509-519. Pdf bib
Classification with Fuzzy Association Rules CFAR-C Z. Chen, G. Chen. Building an associative classifier based on fuzzy association rules. International Journal of Computational Intelligence Systems 1:3 (2008) 262-273. Pdf bib
Fuzzy Association Rule-based Classification method for High-Dimensional problems FARC-HD-C J. Alcala-Fdez, R. Alcalá, F. Herrera. A Fuzzy Association Rule-Based Classification Model for High-Dimensional Problems with Genetic Rule Selection and Lateral Tuning. IEEE Transactions on Fuzzy Systems 19:5 (2011) 857-872. Pdf bib
Main DECISION TREES
  Full Name    Short Name    Reference  
C4.5 C4.5-C J.R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kauffman, 1993.  bib
Iterative Dicotomizer 3 ID3-C J.R. Quinlan. Induction of Decision Trees. Machine Learning 1 (1986) 81-106. Pdf bib
Classification and Regression Tree CART-C L. Breiman, J.H. Friedman, R.A. Olshen, C.J. Stone. Classification and Regression Trees. Chapman and Hall (Wadsworth, Inc.), 1984.  bib
Supervised Learning In Quest SLIQ-C M. Mehta, R. Agrawal, J. Rissanen. SLIQ: A Fast Scalable Classifier for Data Mining. Proceedings of the 5th International Conference on Extending Database Technology. (1996) 18-32. Pdf bib
Hybrid Decision Tree -Genetic Algorithm DT_GA-C D.R. Carvalho, A.A. Freitas. A hybrid decision tree/genetic algorithm method for data mining. Information Sciences 163:1 (2004) 13-35. Pdf bib
Oblique Decision Tree with Evolutionary Learning DT_Oblique-C E. Cantú-Paz, C. Kamath. Inducing oblique decision trees with evolutionary algorithms. IEEE Transactions on Evolutionary Computation 7:1 (2003) 54-68. Pdf bib
Functional Trees FunctionalTrees-C J. Gama. Functional Trees. Machine Learning 55 (2004) 219-250. Pdf bib
PrUning and BuiLding Integrated in Classification PUBLIC-C R. Rastogi, K. Shim. PUBLIC: A Decision Tree Classifier that Integrates Building and Pruning. Data Mining and Knowledge Discovery 4:4 (2000) 315-344. Pdf bib
Tree Analysis with Randomly Generated and Evolved Trees Target-C J.B. Gray, G. Fan. Classification tree analysis using TARGET. Computational Statistics and Data Analysis 52:3 (2008) 1362-1372. Pdf bib
Adaptive Boosting Negative Correlation Learning Extension with C4.5 Decision Tree as Base Classifier AdaBoost.NC-C S. Wang, X. Yao. Multiclass Imbalance Problems: Analysis and Potential Solutions. IEEE Transactions on Systems, Man and Cybernetics Part B 42:4 (2012) 1119-1130. Pdf bib
J.R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kauffman, 1993.  bib
Multiclassifier learning approach (One-vs-One / One-vs-All) with C4.5 as baseline algorithm C45_Binarization-C M. Galar, A. Fernández, E. Barrenechea, H. Bustince, F. Herrera. An Overview of Ensemble Methods for Binary Classifiers in Multi-class Problems: Experimental Study on One-vs-One and One-vs-All Schemes. Pattern Recognition 44:8 (2011) 1761-1776. Pdf bib
M. Galar, A. Fernández, E. Barrenechea, H. Bustince, F. Herrera. Dynamic Classifier Selection for One-vs-One Strategy: Avoiding Non-Competent Classifiers. Pattern Recognition 46:12 (2013) 3412-3424. Pdf bib
J.R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kauffman, 1993.  bib
Main PROTOTYPE SELECTION
  Full Name    Short Name    Reference  
All-KNN AllKNN-C I. Tomek. An Experiment With The Edited Nearest-Neighbor Rule. IEEE Transactions on Systems, Man and Cybernetics 6:6 (1976) 448-452. Pdf bib
Edited Nearest Neighbor ENN-C D.L. Wilson. Asymptotic Properties Of Nearest Neighbor Rules Using Edited Data. IEEE Transactions on Systems, Man and Cybernetics 2:3 (1972) 408-421. Pdf bib
Multiedit Multiedit-C P.A. Devijver. On the editing rate of the MULTIEDIT algorithm. Pattern Recognition Letters 4:1 (1986) 9-12. Pdf bib
Condensed Nearest Neighbor CNN-C P.E. Hart. The Condensed Nearest Neighbour Rule. IEEE Transactions on Information Theory 14:5 (1968) 515-516. Pdf bib
Tomek's modification of Condensed Nearest Neighbor TCNN-C I. Tomek. Two modifications of CNN. IEEE Transactions on Systems, Man and Cybernetics 6 (1976) 769-772. Pdf bib
Instance Based 3 IB3-C D.W. Aha, D. Kibler, M.K. Albert. Instance-Based Learning Algorithms. Machine Learning 6:1 (1991) 37-66. Pdf bib
Modified Edited Nearest Neighbor MENN-C K. Hattori, M. Takahashi. A new edited k-nearest neighbor rule in the pattern classification problem. Pattern Recognition 33 (2000) 521-528. Pdf bib
Modified Condensed Nearest Neighbor MCNN-C V.S. Devi, M.N. Murty. An incremental prototype set building technique. Pattern Recognition 35 (2002) 505-513. Pdf bib
Decremental Reduction Optimization Procedure 3 DROP3-C D.R. Wilson, T.R. Martinez. Reduction Tecniques For Instance-Based Learning Algorithms. Machine Learning 38:3 (2000) 257-286. Pdf bib
Iterative Case Filtering ICF-C H. Brighton, C. Mellish. Advances In Instance Selection For Instance-Based. Data mining and Knowledge Discovery 6:2 (2002) 153-172. Pdf bib
Reduced Nearest Neighbor RNN-C G.W. Gates. The Reduced Nearest Neighbour Rule. IEEE Transactions on Information Theory 18:3 (1972) 431-433. Pdf bib
Selective Nearest Neighbor SNN-C G.L. Ritter, H.B. Woodruff, S.R. Lowry, T.L. Isenhour. An Algorithm For A Selective Nearest Neighbor Decision Rule. IEEE Transactions on Information Theory 21:6 (1975) 665-669. Pdf bib
Variable Similarity Metric VSM-C D.G. Lowe. Similarity Metric Learning For A Variable-Kernel Classifier. Neural Computation 7:1 (1995) 72-85. Pdf bib
Prototype Selection based on Clustering PSC-C J.A. Olvera-López, J.A. Carrasco-Ochoa, J.F. Martínez-Trinidad. A new fast prototype selection method based on clustering. Pattern Analysis and Applications 13 (2010) 131-141. Pdf bib
Model Class Selection ModelCS-C C.E. Brodley. Adressing The Selective Superiority Problem: Automatic Algorithm/Model Class Selection. 10th International Machine Learning Conference (ICML'93). Amherst (MA USA, 1993) 17-24. Pdf bib
Shrink Shrink-C D. Kibler, D.W. Aha. Learning Representative Exemplars Of Concepts: An Initial Case Study. 4th International Workshop on Machine Learning (ML'87 ). Irvine (CA USA, 1987) 24-30.  bib
Minimal Consistent Set MCS-C B.V. Dasarathy. Minimal Consistent Set (MCS) Identification for Optimal Nearest Neighbor Decision Systems Design. IEEE Transactions on Systems, Man and Cybernetics 24:3 (1994) 511-517. Pdf bib
Random Mutation Hill Climbing RMHC-C D.B. Skalak. Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms. 11th International Conference on Machine Learning (ML'94). New Brunswick (NJ USA, 1994) 293-301. Pdf bib
Noise Removing Minimal Consistent Set NRMCS-C X.-Z. Wang, B. Wu, Y.-L. He, X.-H. Pei. NRMCS: Noise removing based on the MCS. 7th International Conference on Machine Learning and Cybernetics (ICMLA08). La Jolla Village (USA, 2008) 89-93. Pdf bib
Class Conditional Instance Selection CCIS-C E. Marchiori. Class Conditional Nearest Neighbor for Large Margin Instance Selection. IEEE Transactions on Pattern Analysis and Machine Intelligence 32:2 (2010) 364-370. Pdf bib
Mutual Neighborhood Value Condensed Nearest Neighbor MNV-C K. Chidananda-Gowda, G. Krishna. The Condensed Nearest Neighbor Rule Using Concept of Mutual Nearest Neighborhood. IEEE Transactions on Information Theory 25:4 (1979) 488-490. Pdf bib
Encoding Length Explore Explore-C R.M. Cameron-Jones. Instance selection by encoding length heuristic with random mutation hill climbing. 8th Australian Joint Conference on Artificial Intelligence (AJCAI-95). (Australia, 1995) 99-106.  bib
Prototipe Selection based on Gabriel Graphs GG-C J.S. Sánchez, F. Pla, F.J. Ferri. Prototype selection for the nearest neighbor rule through proximity graphs. Pattern Recognition Letters 18 (1997) 507-513. Pdf bib
Prototipe Selection based on Relative Neighbourhood Graphs RNG-C J.S. Sánchez, F. Pla, F.J. Ferri. Prototype selection for the nearest neighbor rule through proximity graphs. Pattern Recognition Letters 18 (1997) 507-513. Pdf bib
Nearest Centroid Neighbourhood Edition NCNEdit-C J.S. Sánchez, R. Barandela, A.I. Márques, R. Alejo, J. Badenas. Analysis of new techniques to obtain quality training sets. Pattern Recognition Letters 24 (2003) 1015-1022. Pdf bib
Tabu Search for Instance Selection ZhangTS-C H. Zhang, G. Sun. Optimal reference subset selection for nearest neighbor classification by tabu search. Pattern Recognition 35 (2002) 1481-1490. Pdf bib
Prototipe Selection by Relative Certainty Gain PSRCG-C M. Sebban, R. Nock, S. Lallich. Stopping Criterion for Boosting-Based Data Reduction Techniques: from Binary to Multiclass Problems. Journal of Machine Learning Research 3 (2002) 863-885. Pdf bib
C-Pruner CPruner-C K.P Zhao, S.G. Zhou, J.H. Guan, A.Y. Zhou. C-Pruner: An improved instance prunning algorithm. Second International Conference on Machine Learning and Cybernetics (ICMLC'03). Xian (China, 2003) 94-99. Pdf bib
Pattern by Ordered Projections POP-C J.C. Riquelme, J.S. Aguilar-Ruiz, M. Toro. Finding representative patterns with ordered projections. Pattern Recognition 36 (2003) 1009-1018. Pdf bib
Reconsistent Reconsistent-C M.T. Lozano, J.S. Sánchez, F. Pla. Using the geometrical distribution of prototypes for training set condesing. 10th Conference of the Spanish Association for Artificial Intelligence (CAEPIA03). LNCS 3040, Springer 2003, Malaga (Spain, 2003) 618-627. Pdf bib
Edited NRBF ENRBF-C M. Grochowski, N. Jankowski. Comparison of instance selection algorithms I. Algorithms survey. VII International Conference on Artificial Intelligence and Soft Computing (ICAISC'04). LNCS 3070, Springer 2004, Zakopane (Poland, 2004) 598-603. Pdf bib
Modified Selective Subset MSS-C R. Barandela, F.J. Ferri, J.S. Sánchez. Decision boundary preserving prototype selection for nearest neighbor classification. International Journal of Pattern Recognition and Artificial Intelligence 19:6 (2005) 787-806. Pdf bib
Edited Nearest Neighbor with Estimation of Probabilities Threshold ENNTh-C F. Vazquez, J.S. Sánchez, F. Pla. A stochastic approach to Wilson's editing algorithm. 2nd Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA05). LNCS 3523, Springer 2005, Estoril (Portugal, 2005) 35-42. Pdf bib
Support Vector based Prototype Selection SVBPS-C Y. Li, Z. Hu, Y. Cai, W. Zhang. Support vector vased prototype selection method for nearest neighbor rules. I International conference on advances in natural computation (ICNC05). LNCS 3610, Springer 2005, Changsha (Chine, 2005) 528-535. Pdf bib
Backward Sequential Edition BSE-C J.A. Olvera-López, J.F. Martínez-Trinidad, J.A. Carrasco-Ochoa. Edition Schemes Based on BSE. 10th Iberoamerican Congress on Pattern Recognition (CIARP2004). LNCS 3773, Springer 2005, La Havana (Cuba, 2005) 360-367. Pdf bib
Fast Condensed Nearest Neighbor FCNN-C F. Angiulli. Fast nearest neighbor condensation for large data sets classification. IEEE Transactions on Knowledge and Data Engineering 19:11 (2007) 1450-1464. Pdf bib
Hit-Miss Network Iterative Editing HMNEI-C E. Marchiori. Hit Miss Networks with Applications to Instance Selection. Journal of Machine Learning Research 9 (2008) 997-1017. Pdf bib
Generalized Condensed Nearest Neighbor GCNN-C F. Chang, C.C. Lin, C.-J. Lu. Adaptive Prototype Learning Algorithms: Theoretical and Experimental Studies. Journal of Machine Learning Research 7 (2006) 2125-2148. Pdf bib
Improved KNN Condensation IKNN-C Y. Wu, K. Ianakiev, V. Govindaraju. Improved k-nearest neighbor classification. Pattern Recognition 35:10 (2002) 2311-2318. Pdf bib
Template Reduction for KNN TRKNN-C H.A. Fayed, A.F. Atiya. A novel template reduction approach for the K-nearest neighbor method. IEEE Transactions on Neural Networks 20:5 (2009) 890-896. Pdf bib
Main EVOLUTIONARY PROTOTYPE SELECTION
  Full Name    Short Name    Reference  
CHC Adaptative Search for Instance Selection CHC-C J.R. Cano, F. Herrera, M. Lozano. Using Evolutionary Algorithms As Instance Selection For Data Reduction In KDD: An Experimental Study. IEEE Transactions on Evolutionary Computation 7:6 (2003) 561-575. Pdf bib
Generational Genetic Algorithm for Instance Selection GGA-C J.R. Cano, F. Herrera, M. Lozano. Using Evolutionary Algorithms As Instance Selection For Data Reduction In KDD: An Experimental Study. IEEE Transactions on Evolutionary Computation 7:6 (2003) 561-575. Pdf bib
Steady-State Genetic Algorithm for Instance Selection SGA-C J.R. Cano, F. Herrera, M. Lozano. Using Evolutionary Algorithms As Instance Selection For Data Reduction In KDD: An Experimental Study. IEEE Transactions on Evolutionary Computation 7:6 (2003) 561-575. Pdf bib
Cooperative Coevolutionary Instance Selection CoCoIS-C N. García-Pedrajas, J.A. Romero del Castillo, D. Ortiz-Boyer. A cooperative coevolutionary algorithm for instance selection for instance-based learning. Machine Learning 78:3 (2010) 381-420. Pdf bib
Population-Based Incremental Learning PBIL-C J.R. Cano, F. Herrera, M. Lozano. Using Evolutionary Algorithms As Instance Selection For Data Reduction In KDD: An Experimental Study. IEEE Transactions on Evolutionary Computation 7:6 (2003) 561-575. Pdf bib
Intelligent Genetic Algorithm for Edition IGA-C S.Y. Ho, C.C. Liu, S. Liu. Design of an optimal nearest neighbor classifier using an intelligent genetic algorithm. Pattern Recognition Letters 23 (2002) 1495-1503. Pdf bib
Genetic Algorithm for Editing k-NN with MSE estimation, clustered crossover and fast smart mutation GA_MSE_CC_FSM-C R. Gil-Pita, X. Yao. Using a Genetic Algorithm for Editing k-Nearest Neighbor Classifiers. 8th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL07). LNCS 4881, Springer 2007, Daejeon (Korea, 2007) 1141-1150. Pdf bib
Steady-State Memetic Algorithm for Instance Selection SSMA-C S. García, J.R. Cano, F. Herrera. A Memetic Algorithm for Evolutionary Prototype Selection: A Scaling Up Approach. Pattern Recognition 41:8 (2008) 2693-2709. Pdf bib
Coevolution of Instance selection and Weighting schemes for Nearest Neighbor classifiers CIW_NN-C J. Derrac, I. Triguero, S. García, F. Herrera. Integrating Instance Selection, Instance Weighting and Feature Weighting for Nearest Neighbor Classifiers by Co-evolutionary Algorithms. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics 42:5 (2012) 1383-1397. Pdf bib
Evolutionary Feature Selection for fuzzy Rough set based Prototype Selection EFS_RPS-C J. Derrac, N. Verbiest, S. García, C. Cornelis, F. Herrera. On the use of Evolutionary Feature Selection for Improving Fuzzy Rough Set Based Prototype Selection. Soft Computing 17:2 (2013) 223-238. Pdf bib
Evolutionary Instance Selection enhanced by Rough set based Feature Selection EIS_RFS-C J. Derrac, C. Cornelis, S. García, F. Herrera. Enhancing Evolutionary Instance Selection Algorithms by means of Fuzzy Rough Set based Feature Selection. Information Sciences 186:1 (2012) 73-92. Pdf bib
Instance and Feature Selection based on Coopertive Co-evolution IFS_COCO-C J. Derrac, S. García, F. Herrera. IFS-CoCo: Instance and Feature Selection based on Cooperative Coevolution with Nearest Neighbor Rule. Pattern Recognition 43:6 (2010) 2082-2105. Pdf bib
Main FUZZY INSTANCE BASED LEARNING
  Full Name    Short Name    Reference  
Condensed Fuzzy K-Nearest Neighbors classifier CFKNN-C J.-H. Zhai, N. Li, M.-Y. Zhai. The condensed fuzzy k-nearest neighbor rule based on sample fuzzy entropy. Proceedings of the 2011 International Conference on Machine Learning and Cybernetics (ICMLC'2011). Guilin (China, 2011) 282-286. Pdf bib
Dempster-Shafer theory based K-Nearest Neighbors classifier D_SKNN-C T. Denoeux. A k-nearest neighbor classification rule based on Dempster-Shafer theory. IEEE Transactions on Systems, Man, and Cybernetics 25:5 (1995) 804-813. Pdf bib
Fuzzy C-Means K-Nearest Neighbors classifier FCMKNN-C J. Bedzek, S.K. Chuah, D. Leep. Generalized k-nearest neighbor rules. Fuzzy Sets and Systems 18:3 (1986) 237-256. Pdf bib
Fuzzy Edited K-Nearest Neighbors classifier FENN-C J. Bedzek, S.K. Chuah, D. Leep. Generalized k-nearest neighbor rules. Fuzzy Sets and Systems 18:3 (1986) 237-256. Pdf bib
Fuzzy Rough K-Nearest Neighbors Approach FRKNNA-C H. Bian, L. Mazlack. Fuzzy-rough nearest neighbor classification approach. Proceedings of the 22th International Conference of the North American Fuzzy Information Processing Society (NAFIPS'03). Chicago (Illinois USA, 2003) 500-505. Pdf bib
Fuzzy-Rough Nearest Neighbor algorithm FRNN-C M. Sarkar. Fuzzy-rough nearest neighbor algorithms in classification. Fuzzy Sets and Systems 158:19 (2007) 2134-2152. Pdf bib
Fuzzy-Rough Nearest Neighbor classifier - Fuzzy Rough Sets FRNN_FRS-C R. Jensen, C. Cornelis. Fuzzy-rough nearest neighbour classification. In: A. Skowron, J.F. Peters, C.-C. Chan, J.W. Grzymala-Busse, W. Ziarko (Eds.) Transactions on Rough Sets XIII, 2011, 56-72. Pdf bib
Fuzzy-Rough Nearest Neighbor classifier - Vaguely Quantified Rough Sets FRNN_VQRS-C R. Jensen, C. Cornelis. Fuzzy-rough nearest neighbour classification. In: A. Skowron, J.F. Peters, C.-C. Chan, J.W. Grzymala-Busse, W. Ziarko (Eds.) Transactions on Rough Sets XIII, 2011, 56-72. Pdf bib
Fuzzy K-Nearest Neighbors classifier FuzzyKNN-C J.M. Keller, M.R. Gray, J.A. Givens. A fuzzy k-nearest neighbor algorithm. IEEE Transactions on Systems, Man, and Cybernetics 15:4 (1985) 580-585. Pdf bib
Fuzzy Nearest Prototype classifier FuzzyNPC-C J.M. Keller, M.R. Gray, J.A. Givens. A fuzzy k-nearest neighbor algorithm. IEEE Transactions on Systems, Man, and Cybernetics 15:4 (1985) 580-585. Pdf bib
Genetic Algorithm for Fuzzy K-Nearest Neighbors classifier GAFuzzyKNN-C X. Hu, C. Xie. Improving fuzzy k-nn by using genetic algorithm. Journal of Computational Information Systems 1:2 (2005) 203-213.  bib
Intuitionistic Fuzzy K-Nearest Neighbors classifier IF_KNN-C L.I. Kuncheva. An intuitionistic fuzzy k-nearest neighbors rule. Notes on Intuitionistic Fuzzy Sets 1:1 (1995) 56-60. Pdf bib
Intuitionistic Fuzzy Sets K-Nearest Neighbors classifier IFSKNN-C S. Hadjitodorov. An intuitionistic fuzzy sets application to the k-nn method. Notes on Intuitionistic Fuzzy Sets 1:1 (1995) 66-69. Pdf bib
Intuitionistic Fuzzy Version of K-Nearest Neighbors classifier IFV-NP-C S. Hadjitodorov. An intuitionistic fuzzy version of the nearest prototype classification method, based on a moving-of-pattern procedure. International Journal of General Systems 30:2 (2001) 155-165. Pdf bib
Interval Type-2 Fuzzy K-Nearest Neighbors classifier IT2FKNN-C F.C.-H. Rhee, C. Hwang. An interval type-2 fuzzy k-nearest neighbor. Proceedings of the 12th IEEE International Conference on Fuzzy Systems (FUZZ'03). St Louis (Missouri USA, 2003) 802-807. Pdf bib
Jozwik Fuzzy K-Nearest Neighbor algorithm JFKNN-C A. Jowik. A learning scheme for a fuzzy k-nn rule. Pattern Recoginiton Letters 1:5-6 (1983) 287-289. Pdf bib
Pruned Fuzzy K-Nearest Neighbors classifier PFKNN-C M. Arif, M.U. Akram, F.A. Afsar, F.A.A. Minhas. Pruned fuzzy k-nearest neighbor classifier for beat classification. Journal of Biomedical Science and Engineering 3 (2010) 380-389. Pdf bib
Intuitionistic Fuzzy Version of K-Nearest Neighbors classifier IFV_NP-C S. Hadjitodorov. An intuitionistic fuzzy version of the nearest prototype classification method, based on a moving-of-pattern procedure. International Journal of General Systems 30:2 (2001) 155-165. Pdf bib
Possibilistic instance-based learning PosIBL-C E. Hullermeier. Possibilistic instance-based learning. Artificial Intelligence 148 (2003) 335-383. Pdf bib
Variance Weighted Fuzzy K-Nearest Neighbors VWFuzzyKNN-C J.H. Han, Y.K. Kim. A fuzzy k-nn algorithm using weights from the variance of membership values. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (IEEE CVPR'99). (1999) 394-399. Pdf bib
Main PROTOTYPE GENERATION
  Full Name    Short Name    Reference  
Prototype Nearest Neighbor PNN-C C-L. Chang. Finding Prototypes For Nearest Neighbor Classifiers. IEEE Transactions on Computers 23:11 (1974) 1179-1184. Pdf bib
Learning Vector Quantization 1 LVQ1-C T. Kohonen. The Self-Organizative Map. Proceedings of the IEEE 78:9 (1990) 1464-1480. Pdf bib
Learning Vector Quantization 2 LVQ2-C T. Kohonen. The Self-Organizative Map. Proceedings of the IEEE 78:9 (1990) 1464-1480. Pdf bib
Learning Vector Quantization 2.1 LVQ2_1-C T. Kohonen. The Self-Organizative Map. Proceedings of the IEEE 78:9 (1990) 1464-1480. Pdf bib
Learning Vector Quantization 3 LVQ3-C T. Kohonen. The Self-Organizative Map. Proceedings of the IEEE 78:9 (1990) 1464-1480. Pdf bib
Generalized Editing using Nearest Neighbor GENN-C J. Koplowitz, T.A. Brown. On the relation of performance to editing in nearest neighbor rules. Pattern Recognition 13 (1981) 251-255. Pdf bib
Decision Surface Mapping DSM-C S. Geva, J. Site. Adaptive nearest neighbor pattern classifier. IEEE Transactions on Neural Networks 2:2 (1991) 318-322. Pdf bib
Vector Quantization VQ-C Q. Xie, C.A. Laszlo. Vector quantization technique for nonparametric classifier design. IEEE Transactions on Pattern Analysis and Machine Intelligence 15:12 (1993) 1326-1330. Pdf bib
Chen Algorithm Chen-C C.H. Chen, A. Józwik. A sample set condensation algorithm for the class sensitive artificial neural network. Pattern Recognition Letters 17 (1996) 819-823. Pdf bib
Bootstrap Technique for Nearest Neighbor BTS3-C Y. Hamamoto, S. Uchimura, S. Tomita. A bootstrap technique for nearest neighbor classifier design. IEEE Transactions on Pattern Analysis and Machine Intelligence 19:1 (1997) 73-79. Pdf bib
MSE MSE-C C. Decaestecker. Finding prototypes for nearest neighbour classification by means of gradient descent and deterministic annealing. Pattern Recognition 30:2 (1997) 281-288. Pdf bib
Learning Vector Quantization with Training Counter LVQTC-C R. Odorico. Learning vector quantization with training count (LVQTC). Neural Networks 10:6 (1997) 1083-1088. Pdf bib
Modified Chang's Algorithm MCA-C J.C. Bezdek, T.R. Reichherzer, G.S. Lim, Y. Attikiouzel. Multiple prototype classifier design. IEEE Transactions on Systems, Man and Cybernetics C 28:1 (1998) 67-69. Pdf bib
Generalized Modified Chang's Algorithm GMCA-C R.A. Mollineda, F.J. Ferri, E. Vidal. A merge-based condensing strategy for multiple prototype classifiers. IEEE Transactions on Systems, Man and Cybernetics B 32:5 (2002) 662-668. Pdf bib
Integrated Concept Prototype Learner ICPL-C W. Lam, C.K. Keung, D. Liu. Discovering useful concept prototypes for classification based on filtering and abstraction. IEEE Transactions on Pattern Analysis and Machine Intelligence 14:8 (2002) 1075-1090. Pdf bib
Depuration Algorithm Depur-C J.S. Sánchez, R. Barandela, A.I. Márques, R. Alejo, J. Badenas. Analysis of new techniques to obtain quaylity training sets. Pattern Recognition Letters 24 (2003) 1015-1022. Pdf bib
Hybrid LVQ3 algorithm HYB-C S.-W. Kim, A. Oomenn. A brief taxonomy and ranking of creative prototype reduction schemes. Pattern Analysis and Applications 6 (2003) 232-244. Pdf bib
High training set size reduction by space partitioning and prototype abstraction RSP-C J.S. Sánchez. High training set size reduction by space partitioning and prototype abstraction. Pattern Recognition 37 (2004) 1561-1564. Pdf bib
Evolutionary Nearest Prototype Classifier ENPC-C F. Fernández, P. Isasi. Evolutionary design of nearest prototype classifiers. Journal of Heuristics 10:4 (2004) 431-454. Pdf bib
Adaptive Vector Quantization AVQ-C C.-W. Yen, C.-N. Young, M.L. Nagurka. A vector quantization method for nearest neighbor classifier design. Pattern Recognition Letters 25 (2004) 725-731. Pdf bib
Learning Vector Quantization with pruning LVQPRU-C J. Li, M.T. Manry, C. Yu, D.R. Wilson. Prototype classifier design with pruning. International Journal on Artificial Intelligence Tools 14:1-2 (2005) 261-280. Pdf bib
Pairwise Opposite Class Nearest Neighbor POC-NN-C T. Raicharoen, C. Lursinsap. A divide-and-conquer approach to the pairwise opposite class-nearest neighbor (POC-NN) algorithm. Pattern Recoginiton Letters 26:10 (2005) 1554-1567. Pdf bib
Adaptive Condensing Algorithm Based on Mixtures of Gaussians MixtGauss-C M. Lozano, J.M. Sotoca, J.S. Sánchez, F. Pla, E. Pekalska, R.P.W. Duin. Experimental study on prototype optimisation algorithms for prototype-based classification in vector spaces. Pattern Recognition 39:10 (2006) 1827-1838. Pdf bib
Self-Generating Prototypes SGP-C H.A. Fayed, S.R. Hashem, A.F. Atiya. Self-generating prototypes for pattern classification. Pattern Recognition 40:5 (2007) 1498-1509. Pdf bib
Adaptive Michigan PSO AMPSO-C A. Cervantes, I. Galván, P. Isasi. An Adaptive Michigan Approach PSO for Nearest Prototype Classification. 2nd International Work-Conference on the Interplay Between Natural and Artificial Computation (IWINAC07). LNCS 4528, Springer 2007, La Manga del Mar Menor (Spain, 2007) 287-296. Pdf bib
Prototype Selection Clonal Selection Algorithm PSCSA-C U. Garain. Prototype reduction using an artificial immune model. Pattern Analysis and Applications 11:3-4 (2008) 353-363. Pdf bib
Particle Swarm Optimization PSO-C L. Nanni, A. Lumini. Particle swarm optimization for prototype reduction. Neurocomputing 72:4-6 (2009) 1092-1097. Pdf bib
Nearest subclass classifier NSC-C C.J. Veenman, M.J.T. Reinders. The nearest subclass classifier: A compromise between the nearest mean and nearest neighbor classifier. IEEE Transactions on Pattern Analysis and Machine Intelligence 27:9 (2005) 1417-1429. Pdf bib
Differential Evolution DE-C I. Triguero, S. García, F. Herrera. Differential evolution for optimizing the positioning of prototypes in nearest neighbor classification. Pattern Recognition 44:4 (2011) 901-916. Pdf bib
Scale Factor Local Search in Differential Evolution SFLSDE-C I. Triguero, S. García, F. Herrera. Differential evolution for optimizing the positioning of prototypes in nearest neighbor classification. Pattern Recognition 44:4 (2011) 901-916. Pdf bib
Self-Adaptive Differential Evolution SADE-C I. Triguero, S. García, F. Herrera. Differential evolution for optimizing the positioning of prototypes in nearest neighbor classification. Pattern Recognition 44:4 (2011) 901-916. Pdf bib
Adaptive Differential Evolution with Optional External Archive JADE-C I. Triguero, S. García, F. Herrera. Differential evolution for optimizing the positioning of prototypes in nearest neighbor classification. Pattern Recognition 44:4 (2011) 901-916. Pdf bib
Differential Evolution using a Neighborhood-Based Mutation Operator DEGL-C I. Triguero, S. García, F. Herrera. Differential evolution for optimizing the positioning of prototypes in nearest neighbor classification. Pattern Recognition 44:4 (2011) 901-916. Pdf bib
Hybrid Iterative Case Filtering + Learning Vector Quantization 3 ICFLVQ3-C I. Triguero, S. García, F. Herrera. Differential evolution for optimizing the positioning of prototypes in nearest neighbor classification. Pattern Recognition 44:4 (2011) 901-916. Pdf bib
Hybrid Iterative Case Filtering + Particle Swarm Optimization ICFPSO-C I. Triguero, S. García, F. Herrera. Differential evolution for optimizing the positioning of prototypes in nearest neighbor classification. Pattern Recognition 44:4 (2011) 901-916. Pdf bib
Hybrid Iterative Case Filtering + Scale Factor Local Search in Differential Evolution ICFSFLSDE-C I. Triguero, S. García, F. Herrera. Differential evolution for optimizing the positioning of prototypes in nearest neighbor classification. Pattern Recognition 44:4 (2011) 901-916. Pdf bib
Hybrid Steady-State Memetic Algorithm for Instance Selection + Learning Vector Quantization 3 SSMALVQ3-C I. Triguero, S. García, F. Herrera. Differential evolution for optimizing the positioning of prototypes in nearest neighbor classification. Pattern Recognition 44:4 (2011) 901-916. Pdf bib
Hybrid Steady-State Memetic Algorithm for Instance Selection + Particle Swarm Optimization SSMAPSO-C I. Triguero, S. García, F. Herrera. Differential evolution for optimizing the positioning of prototypes in nearest neighbor classification. Pattern Recognition 44:4 (2011) 901-916. Pdf bib
Hybrid Steady-State Memetic Algorithm for Instance Selection + Scale Factor Local Search in Differential Evolution SSMASFLSDE-C I. Triguero, S. García, F. Herrera. Differential evolution for optimizing the positioning of prototypes in nearest neighbor classification. Pattern Recognition 44:4 (2011) 901-916. Pdf bib
Hybrid Decremental Reduction Optimization Procedure 3 + Learning Vector Quantization 3 DROP3LVQ3-C I. Triguero, S. García, F. Herrera. Differential evolution for optimizing the positioning of prototypes in nearest neighbor classification. Pattern Recognition 44:4 (2011) 901-916. Pdf bib
Hybrid Decremental Reduction Optimization Procedure 3 + Particle Swarm Optimization DROP3PSO-C I. Triguero, S. García, F. Herrera. Differential evolution for optimizing the positioning of prototypes in nearest neighbor classification. Pattern Recognition 44:4 (2011) 901-916. Pdf bib
Hybrid Decremental Reduction Optimization Procedure 3 + Scale Factor Local Search in Differential Evolution DROP3SFLSDE-C I. Triguero, S. García, F. Herrera. Differential evolution for optimizing the positioning of prototypes in nearest neighbor classification. Pattern Recognition 44:4 (2011) 901-916. Pdf bib
Iterative Prototype Adjustment based on Differential Evolution IPLDE-C I. Triguero, S. García, F. Herrera. IPADE: Iterative Prototype Adjustment for Nearest Neighbor Classification. IEEE Transactions on Neural Networks 21:12 (2010) 1984-1990. Pdf bib
Opposition-Based Differential Evolution OBDE-C I. Triguero, S. García, F. Herrera. Differential evolution for optimizing the positioning of prototypes in nearest neighbor classification. Pattern Recognition 44:4 (2011) 901-916. Pdf bib
R.S. Rahnamayan, H.R. Tizhoosh, M.M.A. Salama. Opposition-based differential evolution. IEEE Transactions on Evolutionary Computation 12:1 (2008) 64-79. Pdf bib
Main LAZY LEARNING
  Full Name    Short Name    Reference  
K-Nearest Neighbors Classifier KNN-C T.M. Cover, P.E. Hart. Nearest Neighbor Pattern Classification. IEEE Transactions on Information Theory 13 (1967) 21-27. Pdf bib
Adaptive KNN Classifier KNNAdaptive-C J. Wang, P. Neskovic, L.N. Cooper. Improving nearest neighbor rule with a simple adaptative distance measure.. Pattern Recognition Letters 28 (2007) 207-213. Pdf bib
K * Classifier KStar-C J.G. Cleary, L.E. Trigg. K*: An instance-based learner using an entropic distance measure. Proceedings of the 12th International Conference on Machine Learning. (1995) 108-114. Pdf bib
Lazy Decision Tree LazyDT-C J.H. Friedman, R. Kohavi, Y. Tun. Lazy decision trees. Proceedings of the Thirteenth National Conference on Artificial Intellgence. (1996) 717-724. Pdf bib
Nearest Mean classifier NM-C T. Hastie, R. Tibshirani, J. Friedman. The elements of statistical learning: Data mining, inference, and prediction. Springer-Verlag, 2001. ISBN: 0-387-95284-5.  bib
Cam weighted distance Nearest Neighbor Classifier CamNN-C C. Zhou, Y. Chen. Improving nearest neighbor classification with cam weighted distance. Pattern Recognition 39 (2006) 635-645. Pdf bib
Center Nearest Neighbor Classifier CenterNN Q. Gao, Z. Wang. Center-based nearest neighbor classifier. Pattern Recognition 40 (2007) 346-349. Pdf bib
K Symmetrical Nearest Neighbor Classifier KSNN-C R. Nock, M. Sebban, D. Bernard. A simple locally adaptive nearest neighbor rule with application to pollution forecasting. International Journal of Pattern Recognition and Artificial Intelligence 17 (2003) 1369-1382. Pdf bib
Decision making by Emerging Patterns Classifier Deeps-C J. Li, G. Dong, K. Ramamohanarao, L. Wong. DeEPs: A New Instance-Based Lazy Discovery and Classification System. Machine Learning 54 (2004) 99-124. Pdf bib
Decision making by Emerging Patterns Classifier + Nearest Neighbor Classifier DeepsNN-C J. Li, G. Dong, K. Ramamohanarao, L. Wong. DeEPs: A New Instance-Based Lazy Discovery and Classification System. Machine Learning 54 (2004) 99-124. Pdf bib
Lazy Bayesian Rules classifier LBR-C Z. Zheng, G.I. Webb. Lazy Learning of Bayesian Rules. Machine Learning 41 (2000) 53-87. Pdf bib
Integrated Decremental Instance Based Learning IDIBL D.R. Wilson, T.R. Martinez. An Integrated Instance-Based Learning Algorithm. Computational Intelligence 16:1 (2000) 1-28. Pdf bib
Prototype weigthed classifier PW-C R. Paredes, E. Vidal. Learning weighted metrics to minimize nearest-neighbor classification error. IEEE Transactions on Pattern Analysis and Machine Intelligence 28:7 (2006) 1100-1110. Pdf bib
Class weigthed classifier CW-C R. Paredes, E. Vidal. Learning weighted metrics to minimize nearest-neighbor classification error. IEEE Transactions on Pattern Analysis and Machine Intelligence 28:7 (2006) 1100-1110. Pdf bib
Class and Prototype weigthed classifier CPW-C R. Paredes, E. Vidal. Learning weighted metrics to minimize nearest-neighbor classification error. IEEE Transactions on Pattern Analysis and Machine Intelligence 28:7 (2006) 1100-1110. Pdf bib
Main NEURAL NETWORKS FOR CLASSIFICATION
  Full Name    Short Name    Reference  
Radial Basis Function Neural Network for Classification Problems RBFN-C D.S. Broomhead, D. Lowe. Multivariable Functional Interpolation and Adaptive Networks. Complex Systems 11 (1988) 321-355. Pdf bib
Incremental Radial Basis Function Neural Network for Classification Problems Incr-RBFN-C J. Plat. A Resource Allocating Network for Function Interpolation. Neural Computation 3:2 (1991) 213-225. Pdf bib
Self Optimizing Neural Networks SONN-C I.G. Smotroff, D.H. Friedman, D. Connolly. Self Organizing Modular Neural Networks. Seattle International Joint Conference on Neural Networks (IJCNN'91). Seattle (USA, 1991) 187-192. Pdf bib
Multilayer Perceptron with Conjugate Gradient Based Training MLP-CG-C F. Moller. A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks 6 (1990) 525-533. Pdf bib
B. Widrow, M.A. Lehr. 30 Years of Adaptive Neural Networks: Perceptron, Madaline, and Backpropagation. Proceedings of the IEEE 78:9 (1990) 1415-1442. Pdf bib
Decremental Radial Basis Function Neural Network for Classification Problems Decr-RBFN-C D.S. Broomhead, D. Lowe. Multivariable Functional Interpolation and Adaptive Networks. Complex Systems 11 (1988) 321-355. Pdf bib
Ensemble Neural Network for Classification Problems Ensemble-C N. García-Pedrajas, C. García-Osorio, C. Fyfe. Nonlinear Boosting Projections for Ensemble Construction. Journal of Machine Learning Research 8 (2007) 1-33. Pdf bib
Learning Vector Quantization for Classification Problems LVQ-C J.C. Bezdek, L.I. Kuncheva. Nearest prototype classifier designs: An experimental study. International Journal of Intelligent Systems 16:12 (2001) 1445-1473. Pdf bib
Evolutionary Radial Basis Function Neural Networks EvRBFN-C V.M. Rivas, J.J. Merelo, P.A. Castillo, M.G. Arenas, J.G. Castellano. Evolving RBF neural networks for time-series forecasting with EvRBF. Information Sciences 165:3-4 (2004) 207-220. Pdf bib
Improved Resilient backpropagation Plus iRProp+-C C. Igel, M. Husken. Empirical evaluation of the improved Rprop learning algorithm. Neurocomputing 50 (2003) 105-123. Pdf bib
L.R. Leerink, C.L. Giles, B.G. Horne, M.A. Jabri. Learning with Product Units. In: D. Touretzky, T. Leen (Eds.) Advances in Neural Information Processing Systems, 1995, 537-544. Pdf bib
Multilayer Perceptron with Backpropagation Training MLP-BP-C R. Rojas, J. Feldman. Neural Networks: A Systematic Introduction . Springer-Verlag, Berlin, New-York, 1996. ISBN: 978-3540605058.  bib
Main EVOLUTIONARY NEURAL NETWORKS FOR CLASSIFICATION
  Full Name    Short Name    Reference  
Neural Network Evolutionary Programming for Classification NNEP-C F.J. Martínez-Estudillo, C. Hervás-Martínez, P.A. Gutiérrez, A.C. Martínez-Estudillo. Evolutionary Product-Unit Neural Networks Classifiers. Neurocomputing 72:1-3 (2008) 548-561. Pdf bib
Genetic Algorithm with Neural Network GANN-C G.F. Miller, P.M. Todd, S.U. Hedge. Designing Neural Networks Using Genetic Algorithms. 3rd International Conference on Genetic Algorithm and Their Applications. George Mason University (USA, 1989) 379-384.  bib
X. Yao. Evolving Artificial Neural Networks. Proceedings of the IEEE 87:9 (1999) 1423-1447. Pdf bib
Main SUPPORT VECTOR MACHINES FOR CLASSIFICATION
  Full Name    Short Name    Reference  
C-SVM C_SVM-C C. Cortes, V. Vapnik. Support vector networks. Machine Learning 20 (1995) 273-297. Pdf bib
NU-SVM NU_SVM-C B. Scholkopf, A.J. Smola, R. Williamson, P.L. Bartlett. New support vector algorithms. Neural Computation 12 (2000) 1207-1245. Pdf bib
Sequential Minimal Optimization SMO-C J. Platt. Fast Training of Support Vector Machines using Sequential Minimal Optimization. In: B. Schoelkopf, C. Burges, A. Smola (Eds.) Advances in Kernel Methods - Support Vector Learning, 1998, 185-208. Pdf bib
S.S. Keerthi, S.K. Shevade, C. Bhattacharyya, K.R.K. Murthy. Improvements to Platt's SMO Algorithm for SVM Classifier Design. Neural Computation 13:3 (2001) 637-649. Pdf bib
T. Hastie, R. Tibshirani. Classification by Pairwise Coupling. In: M.I. Jordan, M.J. Kearns, S.A. Solla (Eds.) Advances in Neural Information Processing Systems, 1998, 451-471. Pdf bib
Main STATISTICAL CLASSIFIERS
  Full Name    Short Name    Reference  
Naïve-Bayes NB-C P. Domingos, M. Pazzani. On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning 29 (1997) 103-137. Pdf bib
M.E. Maron. Automatic Indexing: An Experimental Inquiry. Journal of the ACM (JACM) 8:3 (1961) 404-417. Pdf bib
Linear Discriminant Analysis LDA-C G.J. McLachlan. Discriminant Analysis and Statistical Pattern Recognition. John Wiley and Sons, 2004.  bib
R.A. Fisher. The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics 7 (1936) 179-188. Pdf bib
J.H. Friedman. Regularized Discriminant Analysis. Journal of the American Statistical Association 84 (1989) 165-175. Pdf bib
Kernel Classifier Kernel-C G.J. McLachlan. Discriminant Analysis and Statistical Pattern Recognition. John Wiley and Sons, 2004.  bib
Least Mean Square Linear Classifier LinearLMS-C J.S. Rustagi. Optimization Techniques in Statistics. Academic Press, 1994.  bib
Quadratic Discriminant Analysis QDA-C G.J. McLachlan. Discriminant Analysis and Statistical Pattern Recognition. John Wiley and Sons, 2004.  bib
R.A. Fisher. The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics 7 (1936) 179-188. Pdf bib
J.H. Friedman. Regularized Discriminant Analysis. Journal of the American Statistical Association 84 (1989) 165-175. Pdf bib
Least Mean Square Quadratic classifier PolQuadraticLMS-C J.S. Rustagi. Optimization Techniques in Statistics. Academic Press, 1994.  bib
Multinomial logistic regression model with a ridge estimator Logistic-C S. le Cessie, J.C. van Houwelingen. Ridge Estimators in Logistic Regression. Applied Statistics 41:1 (1992) 191-201. Pdf bib
Particle Swarm Optimization - Linear Discriminant Analysis PSOLDA-C S.W. Lin, S.C. Chen. PSOLDA: A particle swarm optimization approach for enhancing classification accuracy rate of linear discriminant analysis. Applied Soft Computing 9 (2009) 1008-1015. Pdf bib



Main Regression Algorithms

Main FUZZY RULE LEARNING FOR REGRESSION
  Full Name    Short Name    Reference  
Fuzzy and Random Sets Based Modeling FRSBM-R L. Sánchez. A Random Sets-Based Method for Identifying Fuzzy Models. Fuzzy Sets and Systems 98:3 (1998) 343-354. Pdf bib
Fuzzy Rule Learning, Wang-Mendel Algorithm WM-R L.X. Wang, J.M. Mendel. Generating Fuzzy Rules by Learning from Examples. IEEE Transactions on Systems, Man and Cybernetics 22:6 (1992) 1414-1427. Pdf bib
Main EVOLUTIONARY FUZZY RULE LEARNING FOR REGRESSION
  Full Name    Short Name    Reference  
Iterative Rule Learning of TSK Rules TSK-IRL-R O. Cordón, F. Herrera. A Two-Stage Evolutionary Process for Designing TSK Fuzzy Rule-Based Systems. IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics 29:6 (1999) 703-715. Pdf bib
Iterative Rule Learning of Mamdani Rules - Small Constrained Approach MOGUL-IRLSC-R O. Cordón, F. Herrera. A Three-Stage Evolutionary Process for Learning Descriptive and Approximate Fuzzy Logic Controller Knowledge Bases from Examples. International Journal of Approximate Reasoning 17:4 (1997) 369-407. Pdf bib
Fuzzy Learning based on Genetic Programming Grammar Operators GFS-GPG-R L. Sánchez, I. Couso, J.A. Corrales. Combining GP Operators with SA Search to Evolve Fuzzy Rule Based Classifiers. Information Sciences 136:1-4 (2001) 175-191. Pdf bib
Iterative Rule Learning of Mamdani Rules - High Constrained Approach MOGUL-IRLHC-R O. Cordón, F. Herrera. Hybridizing Genetic Algorithms with Sharing Scheme and Evolution Strategies for Designing Approximate Fuzzy Rule-Based Systems. Fuzzy Sets and Systems 118:2 (2001) 235-255. Pdf bib
Learning TSK-Fuzzy Models Based on MOGUL MOGUL-TSK-R R. Alcalá, J. Alcala-Fdez, J. Casillas, O. Cordón, F. Herrera. Local Identification of Prototypes for Genetic Learning of Accurate TSK Fuzzy Rule-Based Systems.. International Journal of Intelligent Systems 22:9 (2007) 909-941. Pdf bib
Fuzzy Learning based on Genetic Programming Grammar Operators and Simulated Annealing GFS-SP-R L. Sánchez, I. Couso, J.A. Corrales. Combining GP Operators with SA Search to Evolve Fuzzy Rule Based Classifiers. Information Sciences 136:1-4 (2001) 175-191. Pdf bib
Genetic Fuzzy Rule Learning, Thrift Algorithm Thrift-R P. Thrift. Fuzzy logic synthesis with genetic algorithms. Proceedings of the Fourth International Conference on Genetic Algorithms (ICGA91). San Diego (United States of America, 1991) 509-513. Pdf bib
Genetic-Based Fuzzy Rule Base Construction and Membership Functions Tuning GFS-RB-MF-R A. Homaifar, E. McCormick. Simultaneous Design of Membership Functions and Rule Sets for Fuzzy Controllers Using Genetic Algorithms. IEEE Transactions on Fuzzy Systems 3:2 (1995) 129-139. Pdf bib
O. Cordón, F. Herrera. A Three-Stage Evolutionary Process for Learning Descriptive and Approximate Fuzzy Logic Controller Knowledge Bases from Examples. International Journal of Approximate Reasoning 17:4 (1997) 369-407. Pdf bib
Iterative Rule Learning of Descriptive Mamdani Rules based on MOGUL MOGUL-IRL-R O. Cordón, F. Herrera. A Three-Stage Evolutionary Process for Learning Descriptive and Approximate Fuzzy Logic Controller Knowledge Bases from Examples. International Journal of Approximate Reasoning 17:4 (1997) 369-407. Pdf bib
Symbiotic-Evolution-based Fuzzy Controller design method SEFC-R C.F. Juang, J.Y. Lin, C.-T. Lin. Genetic reinforcement learning through symbiotic evolution for fuzzy controller design. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 30:2 (2000) 290-302. Pdf bib
Pittsburgh Fuzzy Classifier System #1 P_FCS1-R B. Carse, T.C. Fogarty, A. Munro. Evolving fuzzy rule based controllers using genetic algorithms. Fuzzy Sets and Systems 80:3 (1996) 273-293. Pdf bib
Main DECISION TREES FOR REGRESSION
  Full Name    Short Name    Reference  
M5 M5-R J.R. Quinlan. Learning with Continuous Classes. 5th Australian Joint Conference on Artificial Intelligence (AI92). (Singapore, 1992) 343-348. Pdf bib
I. Wang, I.H. Witten. Induction of model trees for predicting continuous classes. 9th European Conference on Machine Learning. Prague (Czech Republic, 1997) 128-137. Pdf bib
Classification and Regression Tree CART-R L. Breiman, J.H. Friedman, R.A. Olshen, C.J. Stone. Classification and Regression Trees. Chapman and Hall (Wadsworth, Inc.), 1984.  bib
M5Rules M5Rules-R J.R. Quinlan. Learning with Continuous Classes. Proceedings of the 5th Australian Joint Conference on Artificial Intelligence. (1992) 343-348. Pdf bib
I. Wang, I.H. Witten. Induction of model trees for predicting continuous classes. Poster papers of the 9th European Conference on Machine Learning. Prague (Czech Republic, 1997) 128-137. Pdf bib
G. Holmes, M. Hall, E. Frank. Generating Rule Sets from Model Trees. Proceedings of the 12th Australian Joint Conference on Artificial Intelligence: Advanced Topics in Artificial Intelligence. Springer-Verlag. Sydney (Australia, 1999) 1-12. Pdf bib
Main EVOLUTIONARY POSTPROCESSING FRBS: SELECTION AND TUNING
  Full Name    Short Name    Reference  
Global Genetic Tuning of the Fuzzy Partition of Linguistic FRBSs GFS-Ling-T O. Cordón, F. Herrera. A Three-Stage Evolutionary Process for Learning Descriptive and Approximate Fuzzy Logic Controller Knowledge Bases from Examples. International Journal of Approximate Reasoning 17:4 (1997) 369-407. Pdf bib
Approximative Genetic Tuning of FRBSs GFS-Aprox-T F. Herrera, M. Lozano, J.L. Verdegay. Tuning Fuzzy Logic Controllers by Genetic Algorithms. International Journal of Approximate Reasoning 12 (1995) 299-315. Pdf bib
Genetic Selection of Linguistic Rule Bases GFS-RS-T O. Cordón, F. Herrera. A Three-Stage Evolutionary Process for Learning Descriptive and Approximate Fuzzy Logic Controller Knowledge Bases from Examples. International Journal of Approximate Reasoning 17:4 (1997) 369-407. Pdf bib
H. Ishibuchi, K. Nozaki, N. Yamamoto, H. Tanaka. Selecting Fuzzy If-Then Rules for Classification Problems Using Genetic Algorithms. IEEE Transactions on Fuzzy Systems 3:3 (1995) 260-270. Pdf bib
Genetic Tuning of FRBSs Weights GFS-Weight-T R. Alcalá, O. Cordón, F. Herrera. Combining Rule Weight Learning and Rule Selection to Obtain Simpler and More Accurate Linguistic Fuzzy Models. In: J. Lawry, J.G. Shanahan, A.L. Ralescu (Eds.) Modelling with Words, LNCS 2873, 2003, 44-63. Pdf bib
Genetic Selection of rules and rules weight tuning of FRBSs GFS-Weight-RS-T R. Alcalá, O. Cordón, F. Herrera. Combining Rule Weight Learning and Rule Selection to Obtain Simpler and More Accurate Linguistic Fuzzy Models. In: J. Lawry, J.G. Shanahan, A.L. Ralescu (Eds.) Modelling with Words, LNCS 2873, 2003, 44-63. Pdf bib
Genetic-Based New Fuzzy Reasoning Model GFS-GB-NFRM-T D. Park, A. Kandel. Genetic-Based New Fuzzy Reasoning Model with Application to Fuzzy Control. IEEE Transactions on System, Man and Cybernetics, Part B: Cybernetics 24:1 (1994) 39-47. Pdf bib
Local Genetic Lateral and Amplitude Tuning of FRBSs GFS-LLA-T R. Alcalá, J. Alcala-Fdez, M.J. Gacto, F. Herrera. Rule Base Reduction and Genetic Tuning of Fuzzy Systems based on the Linguistic 3-Tuples Representation. Soft Computing 11:5 (2007) 401-419. Pdf bib
Local Genetic Lateral Tuning of FRBSs GFS-LL-T R. Alcalá, J. Alcala-Fdez, F. Herrera. A Proposal for the Genetic Lateral Tuning of Linguistic Fuzzy Systems and Its Interaction With Rule Selection. IEEE Transactions on Fuzzy Systems 15:4 (2007) 616-635. Pdf bib
Local Genetic Lateral and Amplitude-Tuning with rule selection of FRBSs GFS-LLARS-T R. Alcalá, J. Alcala-Fdez, M.J. Gacto, F. Herrera. Rule Base Reduction and Genetic Tuning of Fuzzy Systems based on the Linguistic 3-Tuples Representation. Soft Computing 11:5 (2007) 401-419. Pdf bib
Local Genetic Lateral Tuning with rule selection of FRBSs GFS-LLRS-T R. Alcalá, J. Alcala-Fdez, F. Herrera. A Proposal for the Genetic Lateral Tuning of Linguistic Fuzzy Systems and Its Interaction With Rule Selection. IEEE Transactions on Fuzzy Systems 15:4 (2007) 616-635. Pdf bib
Global Genetic Lateral and Amplitude-Tuning of FRBSs GFS-GLA-T R. Alcalá, J. Alcala-Fdez, M.J. Gacto, F. Herrera. Rule Base Reduction and Genetic Tuning of Fuzzy Systems based on the Linguistic 3-Tuples Representation. Soft Computing 11:5 (2007) 401-419. Pdf bib
Global Genetic Lateral Tuning of FRBSs GFS-GL-T R. Alcalá, J. Alcala-Fdez, F. Herrera. A Proposal for the Genetic Lateral Tuning of Linguistic Fuzzy Systems and Its Interaction With Rule Selection. IEEE Transactions on Fuzzy Systems 15:4 (2007) 616-635. Pdf bib
Global Genetic Lateral and Amplitude-Tuning with rule selection of FRBSs GFS-GLARS-T R. Alcalá, J. Alcala-Fdez, M.J. Gacto, F. Herrera. Rule Base Reduction and Genetic Tuning of Fuzzy Systems based on the Linguistic 3-Tuples Representation. Soft Computing 11:5 (2007) 401-419. Pdf bib
Global Genetic Lateral Tuning with rule selection of FRBSs GFS-GLRS-TS R. Alcalá, J. Alcala-Fdez, F. Herrera. A Proposal for the Genetic Lateral Tuning of Linguistic Fuzzy Systems and Its Interaction With Rule Selection. IEEE Transactions on Fuzzy Systems 15:4 (2007) 616-635. Pdf bib
Main NEURAL NETWORKS FOR REGRESSION
  Full Name    Short Name    Reference  
Multilayer Perceptron with Conjugate Gradient Based Training MLP-CG-R F. Moller. A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks 6 (1990) 525-533. Pdf bib
Radial Basis Function Neural Network RBFN-R D.S. Broomhead, D. Lowe. Multivariable Functional Interpolation and Adaptive Networks. Complex Systems 11 (1998) 321-355. Pdf bib
Incremental Radial Basis Function Neural Network Incr-RBFN-R J. Plat. A Resource Allocating Network for Function Interpolation. Neural Computation 3:2 (1991) 213-225. Pdf bib
Self Optimizing Neural Networks SONN-R I.G. Smotroff, D.H. Friedman, D. Connolly. Self Organizing Modular Neural Networks. Seattle International Joint Conference on Neural Networks (IJCNN'91). Seattle (USA, 1991) 187-192. Pdf bib
Decremental Radial Basis Function Neural Network Decr-RBFN-R D.S. Broomhead, D. Lowe. Multivariable Functional Interpolation and Adaptive Networks. Complex Systems 11 (1988) 321-355. Pdf bib
Multilayer Perceptron with Backpropagation Based Training MLP-BP-R R. Rojas, J. Feldman. Neural Networks: A Systematic Introduction . Springer-Verlag, Berlin, New-York, 1996. ISBN: 978-3540605058.  bib
Improved Resilient backpropagation Plus iRProp+-R C. Igel, M. Husken. Empirical evaluation of the improved Rprop learning algorithm. Neurocomputing 50 (2003) 105-123. Pdf bib
J.H. Wang, Y.W. Yu, J.H. Tsai. On the internal representations of product units. Neural Processing Letters 12:3 (2000) 247-254. Pdf bib
Ensemble Neural Network for Regression Problems Ensemble-R N. García-Pedrajas, C. García-Osorio, C. Fyfe. Nonlinear Boosting Projections for Ensemble Construction. Journal of Machine Learning Research 8 (2007) 1-33. Pdf bib
Main EVOLUTIONARY NEURAL NETWORKS FOR REGRESSION
  Full Name    Short Name    Reference  
Genetic Algorithm with Neural Network GANN-R G.F. Miller, P.M. Todd, S.U. Hedge. Designing Neural Networks Using Genetic Algorithms. 3rd International Conference on Genetic Algorithm and Their Applications. Fairfax (Virginia USA, 1989) 379-384.  bib
X. Yao. Evolving Artificial Neural Networks. Proceedings of the IEEE 87:9 (1999) 1423-1447. Pdf bib
Neural Network Evolutionary Programming NNEP-R A.C. Martínez-Estudillo, F.J. Martínez-Estudillo, C. Hervás-Martínez, N. García. Evolutionary Product Unit based Neural Networks for Regression. Neural Networks 19:4 (2006) 477-486. Pdf bib
Main SUPPORT VECTOR MACHINES FOR REGRESSION
  Full Name    Short Name    Reference  
EPSILON-SVR EPSILON_SVR-R R.E. Fan, P.H. Chen, C.J. Lin. Working set selection using the second order information for training SVM. Journal of Machine Learning Research 6 (2005) 1889-1918. Pdf bib
NU-SVR NU_SVR-R R.E. Fan, P.H. Chen, C.J. Lin. Working set selection using the second order information for training SVM. Journal of Machine Learning Research 6 (2005) 1889-1918. Pdf bib
Main EVOLUTIONARY FUZZY SYMBOLIC REGRESSION
  Full Name    Short Name    Reference  
Symbolic Fuzzy Learning based on Genetic Programming Grammar Operators GFS-GAP-Sym-R L. Sánchez, I. Couso. Fuzzy Random Variables-Based Modeling with GA-P Algorithms. In: B. Bouchon, R.R. Yager, L. Zadeh (Eds.) Information, Uncertainty and Fusion, 2000, 245-256. Pdf bib
Symbolic Fuzzy Learning based on Genetic Programming Grammar Operators and Simulated Annealing GFS-GSP-R L. Sánchez, I. Couso. Fuzzy Random Variables-Based Modeling with GA-P Algorithms. In: B. Bouchon, R.R. Yager, L. Zadeh (Eds.) Information, Uncertainty and Fusion, 2000, 245-256. Pdf bib
L. Sánchez, I. Couso, J.A. Corrales. Combining GP Operators with SA Search to Evolve Fuzzy Rule Based Classifiers. Information Sciences 136:1-4 (2001) 175-191. Pdf bib
Symbolic Fuzzy Learning based on Genetic Programming GFS-GP-R L. Sánchez, I. Couso. Fuzzy Random Variables-Based Modeling with GA-P Algorithms. In: B. Bouchon, R.R. Yager, L. Zadeh (Eds.) Information, Uncertainty and Fusion, 2000, 245-256. Pdf bib
Symbolic Fuzzy-Valued Data Learning based on Genetic Programming Grammar Operators and Simulated Annealing GFS-SAP-Sym-R L. Sánchez, I. Couso. Fuzzy Random Variables-Based Modeling with GA-P Algorithms. In: B. Bouchon, R.R. Yager, L. Zadeh (Eds.) Information, Uncertainty and Fusion, 2000, 245-256. Pdf bib
L. Sánchez, I. Couso, J.A. Corrales. Combining GP Operators with SA Search to Evolve Fuzzy Rule Based Classifiers. Information Sciences 136:1-4 (2001) 175-191. Pdf bib
Main STATISTICAL REGRESSION
  Full Name    Short Name    Reference  
Least Mean Squares Linear Regression LinearLMS-R J.S. Rustagi. Optimization Techniques in Statistics. Academic Press, 1994.  bib
Least Mean Squares Quadratic Regression PolQuadraticLMS-R J.S. Rustagi. Optimization Techniques in Statistics. Academic Press, 1994.  bib



Main Imbalanced Classification

Main OVER-SAMPLING METHODS
  Full Name    Short Name    Reference  
Synthetic Minority Over-sampling TEchnique SMOTE-I N.V. Chawla, K.W. Bowyer, L.O. Hall, W.P. Kegelmeyer. SMOTE: Synthetic Minority Over-sampling TEchnique. Journal of Artificial Intelligence Research 16 (2002) 321-357. Pdf bib
Synthetic Minority Over-sampling TEchnique + Edited Nearest Neighbor SMOTE_ENN-I G.E.A.P.A. Batista, R.C. Prati, M.C. Monard. A study of the behavior of several methods for balancing machine learning training data. SIGKDD Explorations 6:1 (2004) 20-29. Pdf bib
Synthetic Minority Over-sampling TEchnique + Tomek's modification of Condensed Nearest Neighbor SMOTE_TL-I G.E.A.P.A. Batista, R.C. Prati, M.C. Monard. A study of the behavior of several methods for balancing machine learning training data. SIGKDD Explorations 6:1 (2004) 20-29. Pdf bib
ADAptive SYNthetic Sampling ADASYN-I H. He, Y. Bai, E.A. Garcia, S. Li. ADASYN: Adaptive Synthetic Sampling Approach for Imbalanced Learning. 2008 International Joint Conference on Neural Networks (IJCNN08). Hong Kong (Hong Kong Special Administrative Region of the Peo, 2008) 1322-1328. Pdf bib
Borderline-Synthetic Minority Over-sampling TEchnique Borderline_SMOTE-I H. Han, W.Y. Wang, B.H. Mao. Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning. 2005 International Conference on Intelligent Computing (ICIC05). LNCS 3644, Springer 2005, Hefei (China, 2005) 878-887. Pdf bib
Safe Level Synthetic Minority Over-sampling TEchnique Safe_Level_SMOTE-I C. Bunkhumpornpat, K. Sinapiromsaran, C. Lursinsap. Safe-level-SMOTE: Safe-level-synthetic minority over-sampling technique for handling the class imbalanced problem. Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD09). LNCS 5476, Springer 2009, Bangkok (Thailand, 2009) 475-482. Pdf bib
Random over-sampling ROS-I G.E.A.P.A. Batista, R.C. Prati, M.C. Monard. A study of the behavior of several methods for balancing machine learning training data. SIGKDD Explorations 6:1 (2004) 20-29. Pdf bib
Adjusting the Direction Of the synthetic Minority clasS examples ADOMS-I S. Tang, S. Chen. The Generation Mechanism of Synthetic Minority Class Examples. 5th Int. Conference on Information Technology and Applications in Biomedicine (ITAB 2008). Shenzhen (China, 2008) 444-447. Pdf bib
Selective Preprocessing of Imbalanced Data SPIDER-I J. Stefanowski, S. Wilk. Selective pre-processing of imbalanced data for improving classification performance. 10th International Conference in Data Warehousing and Knowledge Discovery (DaWaK2008). LNCS 5182, Springer 2008, Turin (Italy, 2008) 283-292. Pdf bib
Aglomerative Hierarchical Clustering AHC-I G. Cohen, M. Hilario, H. Sax, S. Hugonnet, A. Geissbuhler. Learning from imbalanced data in surveillance of nosocomial infection. Artificial Intelligence in Medicine 37 (2006) 7-18. Pdf bib
Selective Preprocessing of Imbalanced Data 2 SPIDER2-I K. Napierala, J. Stefanowski, S. Wilk. Learning from Imbalanced Data in Presence of Noisy and Borderline Examples. 7th International Conference on Rough Sets and Current Trends in Computing (RSCTC2010). Warsaw (Poland, 2010) 158-167. Pdf bib
Hybrid Preprocessing using SMOTE and Rough Sets Theory SMOTE_RSB-I E. Ramentol, Y. Caballero, R. Bello, F. Herrera. SMOTE-RSB*: A Hybrid Preprocessing Approach based on Oversampling and Undersampling for High Imbalanced Data-Sets using SMOTE and Rough Sets Theory. Knowledge and Information Systems 33:2 (2012) 245-265. Pdf bib
Main UNDER-SAMPLING METHODS
  Full Name    Short Name    Reference  
Tomek's modification of Condensed Nearest Neighbor TL-I I. Tomek. Two modifications of CNN. IEEE Transactions on Systems, Man and Cybernetics 6 (1976) 769-772. Pdf bib
Condensed Nearest Neighbor CNN-I P.E. Hart. The Condensed Nearest Neighbour Rule. IEEE Transactions on Information Theory 14:5 (1968) 515-516. Pdf bib
Random under-sampling RUS-I G.E.A.P.A. Batista, R.C. Prati, M.C. Monard. A study of the behavior of several methods for balancing machine learning training data. SIGKDD Explorations 6:1 (2004) 20-29. Pdf bib
One Sided Selection OSS-I M. Kubat, S. Matwin. Addressing the curse of imbalanced training sets: one-sided selection. 14th International Conference on Machine Learning (ICML97). Tennessee (USA, 1997) 179-186. Pdf bib
Condensed Nearest Neighbor + Tomek's modification of Condensed Nearest Neighbor CNNTL-I G.E.A.P.A. Batista, R.C. Prati, M.C. Monard. A study of the behavior of several methods for balancing machine learning training data. SIGKDD Explorations 6:1 (2004) 20-29. Pdf bib
Neighborhood Cleaning Rule NCL-I J. Laurikkala. Improving Identification of Difficult Small Classes by Balancing Class Distribution . 8th Conference on AI in Medicine in Europe (AIME01). LNCS 2001, Springer 2001, Cascais (Portugal, 2001) 63-66. Pdf bib
Undersampling Based on Clustering SBC-I S. Yen, Y. Lee. Under-sampling approaches for improving prediction of the minority class in an imbalanced dataset. International Conference on Intelligent Computing (ICIC06). Kunming (China, 2006) 731-740. Pdf bib
Class Purity Maximization CPM-I K. Yoon, S. Kwek. An unsupervised learning approach to resolving the data imbalanced issue in supervised learning problems in functional genomics. 5th International Conference on Hybrid Intelligent Systems (HIS05). Rio de Janeiro (Brazil, 2005) 303-308. Pdf bib
Main ALGORITHMIC MODIFICATIONS FOR CLASS IMBALANCE
  Full Name    Short Name    Reference  
Genetic Programming-based learning of COmpact and ACcurate fuzzy rule-based classification systems for High-dimensional problems Hierarchical GP-COACH-H-I V. López, A. Fernández, M.J. del Jesus, F. Herrera. A Hierarchical Genetic Fuzzy System Based On Genetic Programming for Addressing Classification with Highly Imbalanced and Borderline Data-sets. Knowledge-Based Systems 38 (2013) 85-104. Pdf bib
Main COST-SENSITIVE CLASSIFICATION
  Full Name    Short Name    Reference  
C4.5 Cost-Sensitive C45CS-I K.M. Ting. An instance-weighting method to induce cost-sensitive trees. IEEE Transactions on Knowledge and Data Engineering 14:3 (2002) 659-665. Pdf bib
J.R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kauffman, 1993.  bib
Multilayer Perceptron with Backpropagation Training Cost-Sensitive NNCS-I Z.-H. Zhou, X.-Y. Liu. Training cost-sensitive neural networks with methods addressing the class imbalance problem. IEEE Transactions on Knowledge and Data Engineering 18:1 (2006) 63-77. Pdf bib
R. Rojas, J. Feldman. Neural Networks: A Systematic Introduction . Springer-Verlag, Berlin, New-York, 1996. ISBN: 978-3540605058.  bib
C-SVM Cost-Sensitive C_SVMCS-I K. Veropoulos, N. Cristianini, C. Campbell. Controlling the sensitivity of support vector machines. 16th International Joint Conferences on Artificial Intelligence (IJCAI99). Stockholm (Sweden, 1999) 281-288. Pdf bib
Y. Tang, Y.-Q. Zhang, N.V. Chawla. SVMs modeling for highly imbalanced classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 39:1 (2009) 0-288. Pdf bib
Main ENSEMBLES FOR CLASS IMBALANCE
  Full Name    Short Name    Reference  
Cost Sensitive Boosting with C4.5 Decision Tree as Base Classifier AdaC2-I Y. Sun, M. Kamel, A. Wong, Y. Wang. Cost-sensitive boosting for classification of imbalanced data. Pattern Recognition 40 (2007) 3358-3378. Pdf bib
J.R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kauffman, 1993.  bib
Adaptive Boosting with C4.5 Decision Tree as Base Classifier AdaBoost-I Y. Freund, R.E. Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55:1 (1997) 119-139. Pdf bib
J.R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kauffman, 1993.  bib
Adaptive Boosting First Multi-Class Extension with C4.5 Decision Tree as Base Classifier AdaBoostM1-I R.E. Schapire, Y. Singer. Improved boosting algorithms using confidence-rated predictions. Machine Learning 37 (1999) 297-336. Pdf bib
Y. Freund, R.E. Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55:1 (1997) 119-139. Pdf bib
J.R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kauffman, 1993.  bib
Adaptive Boosting Second Multi-Class Extension with C4.5 Decision Tree as Base Classifier AdaBoostM2-I R.E. Schapire, Y. Singer. Improved boosting algorithms using confidence-rated predictions. Machine Learning 37 (1999) 297-336. Pdf bib
Y. Freund, R.E. Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55:1 (1997) 119-139. Pdf bib
J.R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kauffman, 1993.  bib
Bootstrap Aggregating with C4.5 Decision Tree as Base Classifier Bagging-I L. Breiman. Bagging predictors. Machine Learning 24 (1996) 123-140. Pdf bib
J.R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kauffman, 1993.  bib
BalanceCascade Ensemble with C4.5 Decision Tree as Base Classifier BalanceCascade-I X.-Y. Liu, J. Wu, Z.-H. Zhou. Exploratory undersampling for class-imbalance learning. IEEE Transactions on Systems, Man, and Cybernetics, Part B 39:2 (2009) 539-550. Pdf bib
J.R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kauffman, 1993.  bib
Boosting with Data Generation for Imbalanced Data with C4.5 Decision Tree as Base Classifier DataBoost-IM-I H. Guo, H.L. Viktor. Learning from imbalanced data sets with boosting and data generation: the DataBoost-IM approach. SIGKDD Explorations 6:1 (2004) 30-39. Pdf bib
J.R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kauffman, 1993.  bib
EasyEnsemble Ensemble with C4.5 Decision Tree as Base Classifier EasyEnsemble-I X.-Y. Liu, J. Wu, Z.-H. Zhou. Exploratory undersampling for class-imbalance learning. IEEE Transactions on Systems, Man, and Cybernetics, Part B 39:2 (2009) 539-550. Pdf bib
J.R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kauffman, 1993.  bib
Integrating Selective Pre-processing of Imbalanced Data with Ivotes Ensemble with C4.5 Decision Tree as Base Classifier IIVotes-I J. Blaszczynski, M. Deckert, J. Stefanowski, S. Wilk. Integrating selective pre-processing of imbalanced data with ivotes ensemble. 7th International Conference on Rough Sets and Current Trends in Computing (RSCTC2010). LNCS 6086, Springer 2010, Warsaw (Poland, 2010) 148-157. Pdf bib
L. Breiman. Pasting small votes for classification in large databases and on-line. Machine Learning 36 (1999) 85-103. Pdf bib
J.R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kauffman, 1993.  bib
Modified Synthetic Minority Over-sampling TEchnique Bagging with C4.5 Decision Tree as Base Classifier MSMOTEBagging-I S. Wang, X. Yao. Diversity analysis on imbalanced data sets by using ensemble models. IEEE Symposium Series on Computational Intelligence and Data Mining (IEEE CIDM 2009). Nashville TN (USA, 2009) 324-331. Pdf bib
M. Galar, A. Fernández, E. Barrenechea, H. Bustince, F. Herrera. A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews (2011) In press. Pdf bib
J.R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kauffman, 1993.  bib
Modified Synthetic Minority Over-sampling TEchnique Boost with C4.5 Decision Tree as Base Classifier MSMOTEBoost-I S. Hu, Y. Liang, L. Ma, Y. He. MSMOTE: Improving classification performance when training data is imbalanced. 2nd International Workshop on Computer Science and Engineering (WCSE 2009). Qingdao (China , 2009) 13-17. Pdf bib
J.R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kauffman, 1993.  bib
Over-sampling Minority Classes Bagging with C4.5 Decision Tree as Base Classifier OverBagging-I S. Wang, X. Yao. Diversity analysis on imbalanced data sets by using ensemble models. IEEE Symposium Series on Computational Intelligence and Data Mining (IEEE CIDM 2009). Nashville TN (USA, 2009) 324-331. Pdf bib
J.R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kauffman, 1993.  bib
Over-sampling Minority Classes Bagging 2 with C4.5 Decision Tree as Base Classifier OverBagging2-I S. Wang, X. Yao. Diversity analysis on imbalanced data sets by using ensemble models. IEEE Symposium Series on Computational Intelligence and Data Mining (IEEE CIDM 2009). Nashville TN (USA, 2009) 324-331. Pdf bib
J.R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kauffman, 1993.  bib
Random Under-Sampling Boosting with C4.5 Decision Tree as Base Classifier RUSBoost-I C. Seiffert, T. Khoshgoftaar, J. Van Hulse, A. Napolitano. Rusboost: A hybrid approach to alleviating class imbalance. IEEE Transactions on Systems, Man and Cybernetics, Part A 40:1 (2010) 185-197. Pdf bib
J.R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kauffman, 1993.  bib
Synthetic Minority Over-sampling TEchnique Bagging with C4.5 Decision Tree as Base Classifier SMOTEBagging-I S. Wang, X. Yao. Diversity analysis on imbalanced data sets by using ensemble models. IEEE Symposium Series on Computational Intelligence and Data Mining (IEEE CIDM 2009). Nashville TN (USA, 2009) 324-331. Pdf bib
J.R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kauffman, 1993.  bib
Synthetic Minority Over-sampling TEchnique Boosting with C4.5 Decision Tree as Base Classifier SMOTEBoost-I N.V. Chawla, A. Lazarevic, L.O. Hall, K.W. Bowyer. SMOTEBoost: Improving prediction of the minority class in boosting. 7th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD 2003). Cavtat Dubrovnik (Croatia, 2003) 107-119. Pdf bib
J.R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kauffman, 1993.  bib
Under-sampling Minority Classes Bagging with C4.5 Decision Tree as Base Classifier UnderBagging-I R. Barandela, R.M. Valdovinos, J.S. Sánchez. New applications of ensembles of classifiers. Pattern Analysis and Applications 6 (2003) 245-256. Pdf bib
J.R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kauffman, 1993.  bib
Under-sampling Minority Classes Bagging 2 with C4.5 Decision Tree as Base Classifier UnderBagging2-I R. Barandela, R.M. Valdovinos, J.S. Sánchez. New applications of ensembles of classifiers. Pattern Analysis and Applications 6 (2003) 245-256. Pdf bib
J.R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kauffman, 1993.  bib
Under-sampling Minority Classes Bagging to Over-sampling Minority Classes Bagging with C4.5 Decision Tree as Base Classifier UnderOverBagging-I S. Wang, X. Yao. Diversity analysis on imbalanced data sets by using ensemble models. IEEE Symposium Series on Computational Intelligence and Data Mining (IEEE CIDM 2009). Nashville TN (USA, 2009) 324-331. Pdf bib
J.R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kauffman, 1993.  bib
Adaptive Boosting Negative Correlation Learning Extension with C4.5 Decision Tree as Base Classifier AdaBoost.NC-I S. Wang, X. Yao. Multiclass Imbalance Problems: Analysis and Potential Solutions. IEEE Transactions on Systems, Man and Cybernetics Part B 42:4 (2012) 1119-1130. Pdf bib
J.R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kauffman, 1993.  bib
Ensemble learning classifier with undersampling preprocessing with C4.5 Decision Tree as Base Classifier EUSBoost-I M. Galar, A. Fernández, E. Barrenechea, F. Herrera. EUSBoost: Enhancing Ensembles for Highly Imbalanced Data-sets by Evolutionary Undersampling. Pattern Recognition 46:12 (2013) 3460-3471. Pdf bib
J.R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kauffman, 1993.  bib



Main Semi-Supervised Learning

Main MULTIPLE-CLASSIFIER METHODS
  Full Name    Short Name    Reference  
ADE Coforest ADE_CoForest C. Deng, M. Zuo Guo. A new co-training-style random forest for computer aided diagnosis. Journal of Intelligent Information Systems 36:3 (2011) 253-281. Pdf bib
CLCC CLCC T. Huang, Y. Yu, G. Guo, K. Li. A classification algorithm based on local cluster centers with a few labeled training examples. Knowledge Based Systems 26:6 (2010) 563-571. Pdf bib
CoBC CoBC M. Hady, F. Schwenker, G. Palm. Semi-supervised learning for tree-structured ensembles of RBF networks with Co-Training. Neural Networks 23 (2010) 497-509. Pdf bib
CoForest CoForest M. Li, Z.H. Zhou. Improve Computer-Aided Diagnosis With Machine Learning Techniques Using Undiagnosed Samples. Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on 37:6 (2007) 1088-1098. Pdf bib
CoTraining CoTraining A. Blum, T. Mitchell. Combining labeled and unlabeled data with co-training. Proceedings of the annual ACM conference on computational learning theory. (1998) 92-100. Pdf bib
Differential Evolution TriTraining DE_TriTraining C. Deng, M. Guo. Tri-training and data editing based semi-supervised clustering algorithm. MICAI 2006: Advances in Artificial Intelligence (MICAI 2006). (2006) 641-651. Pdf bib
Random subspace method for co-training RASCO J. Wang, S. Luo, X. Zeng. A random subspace method for co-training. IEEE international joint conference on computational intelligence. (2008) 195-200. Pdf bib
Co-training with relevant random subspaces Rel_RASCO Y. Yaslan, Z. Cataltepe. Co-training with relevant random subspaces. Neurocomputing 73 (2010) 1652-1661. Pdf bib
TriTraining TriTraining Z.H. Zhou, M. Li. Tri-training: exploiting unlabeled data using three classifiers. IEEE Transactions on Knowledge and Data Engineering 17 (2005) 1529-1541. Pdf bib
Democratic co-learning Democratic Y. Zhou, S. Goldman. Democratic co-learning. IEEE international conference on tools with artificial intelligence. (2004) 594-602. Pdf bib
Main SINGLE-CLASSIFIER METHODS
  Full Name    Short Name    Reference  
APSSC APSSC A. Halder, S. Ghosh, A. Ghosh. Aggregation pheromone metaphor for semi-supervised classification. Pattern Recognition 46 (2013) 2239-2248. Pdf bib
Self Training SelfTraining D. Yarowsky. Unsupervised word sense disambiguation rivaling supervised methods. 33rd Annual Meeting of the Association for Computational Liguistics. (1995) 189-196. Pdf bib
Self Training with Editing SETRED M. Li, Z.-H. Zhou. SETRED: self-training with editing. Advances in Knowledge Discovery and Data Mining. LNCS 3518, Springer 2005 (2005) 611-621. Pdf bib
Self-training nearest neighbor rule using cut edges SNNRCE Y. Wang, X. Xu, H. Zhao, Z. Hua. Semi-supervised learning based on nearest neighbor rule and cut edges. Knowledge-Based Systems 23:6 (2010) 547-554. Pdf bib
Main SUPERVISED METHODS
  Full Name    Short Name    Reference  
C4.5 Semi Supervised Learning C4.5SSL J.R. Quinlan. Programs for Machine Learning.. Morgan Kauffman (1993) In press.  bib
Naive Bayes Classifier Algorithm for SSL NBSSL P. Domingos, M. Pazzani. On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning 29 (1997) 103-137. Pdf bib
Nearest Neighbor Algorithm for SSL NNSSL G.J. McLachlan. Discriminant Analysis and Statistical Pattern Recognition. Wiley Series in Probability and Mathematical Statistics, 2004.  bib
SMO for Semi Supervised Learning SMOSSL J.C. Platt. Fast training of support vector machines using sequential minimal optimization. MIT Press, 1999.  bib



Main Subgroup Discovery

Main SUBGROUP DISCOVERY
  Full Name    Short Name    Reference  
CN2 Algorithm for Subgroup Discovery CN2-SD N. Lavrac, B. Kavsek, P. Flach, L. Todorovski.. Subgroup Discovery with CN2-SD. Journal of Machine Learning Research 5 (2004) 153-188. Pdf bib
Apriori Algorithm for Subgroup Discovery Apriori-SD B. Kavsek, N. Lavrac. APRIORI-SD: Adapting Association Rule Learning to Subgroup Discovery. Applied Artificial Intelligence 20:7 (2006) 543-583. Pdf bib
Subgroup Discovery Algorithm SD-Algorithm-SD D. Gambergr, N. Lavrac. Expert-Guided Subgroup Discovery: Methodology and Application. Journal of Artificial Intelligence Research 17 (2002) 501-527. Pdf bib
Subgroup Discovery Iterative Genetic Algorithms SDIGA-SD M.J. del Jesus, P. González, F. Herrera, M. Mesonero. Evolutionary Fuzzy Rule Induction Process for Subgroup Discovery: A case study in marketing. IEEE Transactions on Fuzzy Systems 15:4 (2007) 578-592. Pdf bib
Non-dominated Multi-Objective Evolutionary algorithm for Extracting Fuzzy rules in Subgroup Discovery NMEEF-SD C.J. Carmona, P. González, M.J. del Jesus, F. Herrera. Non-dominated Multi-objective Evolutionary algorithm based on Fuzzy rules extraction for Subgroup Discovery. 4th International Conference on Hybrid Artificial Intelligence Systems (HAIS09). LNCS 5572, Springer 2009, Salamanca (Spain, 2009) 573-580. Pdf bib
MESDIF for Subgroup Discovery MESDIF-SD F.J. Berlanga, M.J. del Jesus, P. González, F. Herrera, M. Mesonero. Multiobjective Evolutionary Induction of Subgroup Discovery Fuzzy Rules: A Case Study in Marketing. 6th Industrial Conference on Data Mining. LNCS 4065, Springer 2006, Leipzig (Germany, 2006) 337-349. Pdf bib
M.J. del Jesus, P. González, F. Herrera. Multiobjective Genetic Algorithm for Extracting Subgroup Discovery Fuzzy Rules. IEEE Symposium on Computational Intelligence in Multicriteria Decision Making. (2007) 0-57. Pdf bib
SD-Map algorithm SDMap-SD M. Atzmueller, F. Puppe. SD-Map - A Fast Algorithm for Exhaustive Subgroup Discovery. 17th European Conference on Machine Learning and 10th European Conference on Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2006). LNCS 4213, Springer 2006, Berlin (Germany, 2006) 6-17. Pdf bib



Main Multi Instance Learning

Main MULTI INSTANCE LEARNING
  Full Name    Short Name    Reference  
Citation K-Nearest Neighbor classifier CitationKNN-M J. Wang, J.D. Zucker. Solving the Multiple-Instance Problem: A Lazy Learning Approach. 17th International Conference on Machine Learning (ICLM2000). Stanford (USA, 2000) 1119-1126. Pdf bib
Diverse Denstiy algorithm DD-M O. Maron, T. Lozano-Pérez. A framework for multiple-instance learning. Neural Information Processing Systems (NIPS97). Denver (USA, 1997) 570-576. Pdf bib
Expectation Maximization Diverse Density EMDD-M J. Wang, J.D. Zucker. Solving the Multiple-Instance Problem: A Lazy Learning Approach. 17th International Conference on Machine Learning (ICLM2000). Stanford (USA, 2000) 1119-1126. Pdf bib
K-Nearest Neighbors for Multiple Instance Learning KNN-MI-M J. Wang, J.D. Zucker. Solving the Multiple-Instance Problem: A Lazy Learning Approach. 17th International Conference on Machine Learning (ICLM2000). Stanford (USA, 2000) 1119-1126. Pdf bib
Grammar-Guided Genetic Programming for Multiple Instance Learning G3P-MI-M A. Zafra, S. Ventura. G3P-MI: A genetic programming algorithm for multiple instance learning. Information Sciences 180:23 (2010) 4496-4513. Pdf bib
Axis Parallel Rectangle using Iterated Discrimination APR_IteratedDiscrimination-M T.G. Dietterich, R.H. Lathrop, T. Lozano-Pérez. Solving the multiple instance problem with axis-parallel rectangles. Artificial Intelligence 89 (1997) 31-71. Pdf bib
Axis Parallel Rectangle using positive vectors covering eliminating negative instances APR_GFS_AllPositive-M T.G. Dietterich, R.H. Lathrop, T. Lozano-Pérez. Solving the multiple instance problem with axis-parallel rectangles. Artificial Intelligence 89 (1997) 31-71. Pdf bib
Axis Parallel Rectangle eliminating negative instances APR_GFS_ElimCount-M T.G. Dietterich, R.H. Lathrop, T. Lozano-Pérez. Solving the multiple instance problem with axis-parallel rectangles. Artificial Intelligence 89 (1997) 31-71. Pdf bib
Axis Parallel Rectangle eliminating negative instances based on a kernel density estimate APR_GFS_Kde-M T.G. Dietterich, R.H. Lathrop, T. Lozano-Pérez. Solving the multiple instance problem with axis-parallel rectangles. Artificial Intelligence 89 (1997) 31-71. Pdf bib



Main Clustering Algorithms

Main CLUSTERING ALGORITHMS
  Full Name    Short Name    Reference  
ClusterKMeans KMeans-CL J.B. MacQueen. Some Methods for Classification and Analysis of Multivariate Observations. 5th Berkeley Symposium on Mathematical Statistics and Probability. Berkeley (USA, 1967) 281-297. Pdf bib



Main Association Rules

Main ASSOCIATION RULES
  Full Name    Short Name    Reference  
Apriori Apriori-A R. Srikant, R. Agrawal. Mining quantitative association rules in large relational tables. ACM SIGMOD International Conference on Management of Data. Montreal Quebec (Canada, 1996) 1-12. Pdf bib
C. Borgelt. Efficient implementations of Apriori and Eclat. Workshop of Frequent Item Set Mining Implementations (FIMI 2003). Florida (USA, 2003) 280-296. Pdf bib
Association Rules Mining by means of a genetic algorithm proposed by Alatas et al. Alatasetal-A B. Alatas, E. Akin. An efficient genetic algorithm for automated mining of both positive and negative quantitative association rules. Soft Computing 10 (2006) 230-237. Pdf bib
Evolutionary Association Rules Mining with Genetic Algorithm EARMGA-A X. Yan, Ch. Zhang, S. Zhang. Genetic algorithm-based strategy for identifying association rules without specifying actual minimum support. Expert Systems with Applications 36:2 (2009) 3066-3076. Pdf bib
Equivalence CLAss Transformation Eclat-A M.J. Zaki. Scalable Algorithms for Association Mining. IEEE Transactions on Knowledge and Data Engineering 12:3 (2000) 372-390. Pdf bib
C. Borgelt. Efficient implementations of Apriori and Eclat. Workshop of Frequent Item Set Mining Implementations (FIMI 2003). Florida (USA, 2003) 280-296. Pdf bib
Frequent Pattern growth FPgrowth-A J. Han, J. Pei, Y. Yin, R. Mao. Mining frequent patterns without candidate generation: A frequent-pattern tree approach. Data Mining and Knowledge Discovery 8:1 (2004) 53-87. Pdf bib
Genetic Association Rules GAR-A J. Mata, J.L. Alvarez, J.C. Riquelme. Discovering numeric association rules via evolutionary algorithm. Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD). Springer, Heidelberg. Hong Kong (China, 2001) 40-51. Pdf bib
J. Mata, J.L. Alvarez, J.C. Riquelme. An evolutionary algorithm to discover numeric association rules. ACM Symposium on Applied Computing. Madrid (Spain, 2002) 0-594. Pdf bib
GENetic Association Rules GENAR-A J. Mata, J.L. Alvarez, J.C. Riquelme. Mining numeric association rules with genetic algorithms. 5th International Conference on Artificial Neural Networks and Genetic Algorithms (ICANNGA). Taipei (Taiwan, 2001) 264-267. Pdf bib
Alcala et al Method Alcalaetal-A J. Alcala-Fdez, R. Alcalá, M.J. Gacto, F. Herrera. Learning the membership function contexts for mining fuzzy association rules by using genetic algorithms. Fuzzy Sets and Systems 160 (2009) 905-921. Pdf bib
Fuzzy Apriori FuzzyApriori-A T.-P. Hong, C.-S. Kuo, S.-C. Chi. Trade-off between computation time and number of rules for fuzzy mining from quantitative data. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 9:5 (2001) 587-604. Pdf bib
Genetic Fuzzy Apriori GeneticFuzzyApriori-A T.-P. Hong, C.-H. Chen, Y.-L. Wu, Y.-C. Lee. A GA-based fuzzy mining approach to achieve a trade-off between number of rules and suitability of membership functions. Soft Computing 10:11 (2006) 1091-1101. Pdf bib
Genetic-Fuzzy Data Mining With Divide-and-Conquer Strategy GeneticFuzzyAprioriDC-A T.-P. Hong, C.-H. Chen, Y.-C. Lee, Y.-L. Wu. Genetic-Fuzzy Data Mining With Divide-and-Conquer Strategy. IEEE Transactions on Evolutionary Computation 12:2 (2008) 252-265. Pdf bib
ARMMGA ARMMGA-A H.R. Qodmanan, M. Nasiri, B. Minaei-Bidgoli. Multi objective association rule mining with genetic algorithm without specifying minimum support and minimum confidence. Expert Systems with Applications 38 (2011) 288-298. Pdf bib
Multi-objective differential evolution algorithm for mining numeric association rules MODENAR-A B. Alatas, E. Akin, A. Karci. MODENAR: Multi-objective differential evolution algorithm for mining numeric association rules. Applied Soft Computing 8 (2008) 646-656. Pdf bib
Multi-objective rule mining using genetic algorithms MOEA_Ghosh-A A. Ghosh, B. Nath. Multi-objective rule mining using genetic algorithms. Information Sciences 163 (2004) 123-133. Pdf bib
Multi-Objective Evolutionary Algorithm for Mining a Reduced Set of Interesting Positive and Negative Quantitative Association Rules MOPNAR-A D. Martin, A. Rosete, J. Alcala-Fdez, F. Herrera. A New Multi-Objective Evolutionary Algorithm for Mining a Reduced Set of Interesting Positive and Negative Quantitative Association Rules. IEEE Transaction on Evolutionary Computation 18:1 (2014) 54-69. Pdf bib
QAR_CIP_NSGAII QAR_CIP_NSGAII-A D. Martin, A. Rosete, J. Alcala-Fdez, F. Herrera. QAR-CIP-NSGA-II: A new multi-objective evolutionary algorithm to mine quantitative association rules. Information Sciences 258 (2014) 1-28. Pdf bib
Niching genetic algorithm to mine positive and negative quantitative association rules NICGAR-A D. Martin, J. Alcala-Fdez, A. Rosete, F. Herrera. NICGAR: a Niching Genetic Algorithm to Mine a Diverse Set of Interesting Quantitative Association Rules. Information Sciences 355356 (2016) 208-228. Pdf bib



Main Statistical Tests

Main TEST ANALYSIS
  Full Name    Short Name    Reference  
5x2 Cross validation F-test 5x2CV-ST T.G. Dietterich. Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms. Neural Computation 10:7 (1998) 1895-1923. Pdf bib
Wilcoxon signed ranks test (for a single data-set) Single-Wilcoxon-ST F. Wilcoxon. Individual Comparisons by Ranking Methods. Biometrics 1 (1945) 80-83. Pdf bib
J.P. Royston. Algorithm AS 181. Applied Statistics 31:2 (1982) 176-180. Pdf bib
T-test T-test-ST D.R. Cox, D.V. Hinkley. Theoretical Statistics. Chapman and Hall, 1974.  bib
Snedecor F-test SnedecorF-ST G.W. Snedecor, W.G. Cochran. Statistical Methods. Iowa State University Press, 1989.  bib
Normality Shapiro-Wilk test ShapiroWilk-ST S.S. Shapiro, M.B. Wilk. An Analysis of Variance Test for Normality (complete samples). Biometrika 52:3-4 (1965) 591-611. Pdf bib
Mann-Whitney U-test MannWhitneyU-ST H.B. Mann, D.R. Whitney. On a Test of Whether One of Two Random Variables is Stochastically Larger Than The Other. Annals of Mathematical Statistics 18 (1947) 50-60. Pdf bib
Wilcoxon Signed-Rank Test Wilcoxon-ST F. Wilcoxon. Individual Comparisons by Ranking Methods. Biometrics 1 (1945) 80-83. Pdf bib
J.P. Royston. Algorithm AS 181. Applied Statistics 31:2 (1982) 176-180. Pdf bib
Friedman Test and Post-Hoc Procedures Friedman-ST D. Sheskin. Handbook of parametric and nonparametric statistical procedures. Chapman and Hall/CRC, 2003.  bib
M. Friedman. The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the American Statistical Association 32:200 (1937) 675-701. Pdf bib
Quade Test and Post-Hoc Procedures Quade-ST D. Quade. Using weighted rankings in the analysis of complete blocks with additive block effects. Journal of the American Statistical Association 74 (1979) 680-683. Pdf bib
W.J. Conover. Practical Nonparametric Statistics. Wiley, 1998.  bib
Friedman Aligned Test and Post-Hoc Procedures FriedmanAligned-ST J.L. Hodges, E.L. Lehmann. Ranks methods for combination of independent experiments in analysis of variance. Annals of Mathematical Statistics 33 (1962) 482-497. Pdf bib
W.W. Daniel. Applied Nonparametric Statistics. Houghton Mifflin Harcourt, 1990.  bib
Friedman Test for Multiple Comparisons and Post-Hoc Procedures Multiple-Test-ST R.G.D. Steel. A multiple comparison sign test: treatments versus control. Journal of American Statistical Association 54 (1959) 767-775. Pdf bib
D. Sheskin. Handbook of parametric and nonparametric statistical procedures. Chapman and Hall/CRC, 2003.  bib
M. Friedman. The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the American Statistical Association 32:200 (1937) 675-701. Pdf bib
Contrast estimation Contrast-Test-ST K. Doksum. Robust procedures for some linear models with one observation per cell. Annals of Mathematical Statistics 38 (1967) 878-883. Pdf bib
Main POST-HOC PROCEDURES FOR 1 X N TESTS
  Name    Applicable Statistical Tests    Reference  
Bonferroni-Dunn Post Hoc procedure for 1xN Statistical Tests Friedman-ST, FriedmanAligned-ST, Quade-ST O. Dunn. Multiple comparisons among means. Journal of the American Statistical Association 56 (1961) 52-64. Pdf bib
Holm Post Hoc procedure for 1xN Statistical Tests Friedman-ST, FriedmanAligned-ST, Quade-ST S. Holm. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6 (1979) 65-70.  bib
Hochberg Post Hoc procedure for 1xN Statistical Tests Friedman-ST, FriedmanAligned-ST, Quade-ST Y. Hochberg. A sharper Bonferroni procedure for multiple tests of significance. Biometrika 75 (1988) 800-803. Pdf bib
Hommel Post Hoc procedure for 1xN Statistical Tests Friedman-ST, FriedmanAligned-ST, Quade-ST G. Hommel. A stagewise rejective multiple test procedure based on a modified Bonferroni test. Biometrika 75 (1988) 383-386. Pdf bib
Holland Post Hoc procedure for 1xN Statistical Tests Friedman-ST, FriedmanAligned-ST, Quade-ST B.S. Holland, M.D. Copenhaver. An improved sequentially rejective Bonferroni test procedure. Biometrics 43 (1987) 417-423. Pdf bib
Rom Post Hoc procedure for 1xN Statistical Tests Friedman-ST, FriedmanAligned-ST, Quade-ST D.M. Rom. A sequentially rejective test procedure based on a modified Bonferroni inequality. Biometrika 77 (1990) 663-665.  bib
Finner Post Hoc procedure for 1xN Statistical Tests Friedman-ST, FriedmanAligned-ST, Quade-ST H. Finner. On a monotonicity problem in step-down multiple test procedures. Journal of the American Statistical Association 88 (1993) 920-923. Pdf bib
Li Post Hoc procedure for 1xN Statistical Tests Friedman-ST, FriedmanAligned-ST, Quade-ST J. Li. A two-step rejection procedure for testing multiple hypotheses. Journal of Statistical Planning and Inference 138 (2008) 1521-1527. Pdf bib
Main POST-HOC PROCEDURES FOR N X N TESTS
  Name    Applicable Statistical Tests    Reference  
Nemenyi Post Hoc procedure for NxN Statistical Tests Multiple-Test-ST P.B. Nemenyi. Distribution-free Multiple Comparisons. PhD thesis, Princeton University (1963) -.  bib
Holm Post Hoc procedure for NxN Statistical Tests Multiple-Test-ST S. Holm. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics 6 (1979) 65-70.  bib
Shaffer Post Hoc procedure for NxN Statistical Tests Multiple-Test-ST J.P. Shaffer. Modified sequentially rejective multiple test procedures. Journal of the American Statistical Association 81:395 (1986) 826-831. Pdf bib
Bergman Post Hoc procedure for NxN Statistical Tests Multiple-Test-ST G. Bergmann, G. Hommel. Improvements of general multiple test procedures for redundant systems of hypotheses. In: P. Bauer, G. Hommel, E. Sonnemann (Eds.) Multiple Hypotheses Testing, 1988, 100-115.  bib

                                                                                                                                



 
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