KEEL: A software tool to assess evolutionary algorithms for Data Mining problems (regression, classification, clustering, pattern mining and so on)

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)

Data Preprocessing

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.	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.	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.	bib
Iterative Dicotomizer 3 Discretizer	ID3-D	J.R. Quinlan. Induction of Decision Trees. Machine Learning 1 (1986) 81-106.	bib
Bayesian Discretizer	Bayesian-D	X. Wu. A Bayesian Discretizer for Real-Valued Attributes. The. Computer J. 39:8 (1996) 688-691.	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.	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.	bib
Unparametrized Supervised Discretizer	USD-D	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.	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.	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.	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.	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.	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.	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.	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.	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.	bib
Khiops Discretizer	Khiops-D	M. Boulle. Khiops: A Statistical Discretization Method of Continuous Attributes. Machine Learning 55:1 (2004) 53-69.	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.	bib
MODL Discretizer	MODL-D	M. Boulle. MODL: A bayes optimal discretization method for continuous attributes. Machine Learning 65:1 (2006) 131-165.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	bib
Multivariate Discretization	MVD-D	S.D. Bay. Multivariate Discretization for Set Mining. Knowledge and Information Systems 3 (2001) 491-512.	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.	bib
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.	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.	bib
Las Vegas Filter	LVF-FS	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.	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.	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.	bib
Las Vegas Wrapper	LVW-FS	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.	bib
	ABB-IEP-FS	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.	bib
	ABB-LIU-FS	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.	bib
Automatic Branch and Bound using Mutual Information Measure	ABB-MI-FS	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.	bib
Full Exploration using Inconsistent Examples Pairs Measure	Full-IEP-FS	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.	bib
Full Exploration (LIU)	Full-LIU-FS	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.	bib
Full Exploration using Mutual Information measure	Full-MI-FS	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.	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.	bib
Las Vegas Filter using Inconsistent Examples Pairs Measure	LVF-IEP-FS	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.	bib
	SA-IEP-FS	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.	bib
Simulated Annealing using Inconsistent Examples measure	SA-LIU-FS	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.	bib
Simulated Annealing using Mutual Information measure	SA-MI-FS	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.	bib
	SBS-IEP-FS	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.	bib
	SBS-LIU-FS	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.	bib
Sequential Backward Search using Mutual Information measure	SBS-MI-FS	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.	bib
	SFS-IEP-FS	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.	bib
	SFS-LIU-FS	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.	bib
Sequential Forward Search using Mutual Information measure	SFS-MI-FS	H. Liu, H. Motoda. Feature Selection for Knowledge Discovery and Data Mining. Kluwer Academic Publishers, 1998.	bib
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.	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.	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.	bib
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.	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.	bib
Variable Similarity Metric	VSM-TSS	D.G. Lowe. Similarity Metric Learning For A Variable-Kernel Classifier. Neural Computation 7:1 (1995) 72-85.	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.	bib
Multiedit	Multiedit-TSS	P.A. Devijver. On the editing rate of the MULTIEDIT algorithm. Pattern Recognition Letters 4:1 (1986) 9-12.	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.	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.	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.	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.	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.	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.	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.	bib
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.	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.	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.	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.	bib
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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	bib
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.	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.	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.	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.	bib
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.	bib
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.	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.	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.	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.	bib
Ensemble Filter	EnsembleFilter-F	C.E. Brodley, M.A. Friedl. Identifying Mislabeled Training Data. Journal of Artiﬁcial Intelligence Research 11 (1999) 131-167.	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.	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.	bib

Classification Algorithms

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.	bib
CN2	CN2-C	P. Clark, T. Niblett. The CN2 Induction Algorithm. Machine Learning Journal 3:4 (1989) 261-283.	bib
PRISM	PRISM-C	J. Cendrowska. PRISM: An algorithm for inducing modular rules. International Journal of Man-Machine Studies 27:4 (1987) 349-370.	bib
1R	1R-C	R.C. Holte. Very simple classification rules perform well on most commonly used datasets. Machine Learning 11 (1993) 63-91.	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.	bib
C4.5Rules	C45Rules-C	J.R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kauffman Publishers, 1993.	bib
C4.5Rules	C45Rules-C	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.	bib
C4.5Rules (Simulated Annealing version)	C45RulesSA-C	J.R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kauffman Publishers, 1993.	bib
C4.5Rules (Simulated Annealing version)	C45RulesSA-C	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.	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.	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.	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.	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.	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.	bib
Swap1	Swap1-C	M. Sholom, N. Indurkhya. Optimized Rule Induction. IEEE Expert 1 (1993) 61-70.	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.	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.	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.	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.	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.	bib
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.	bib
	GAssist-ADI-C	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.	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.	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.	bib
X-Classifier System	XCS-C	S.W. Wilson. Classifier Fitness Based on Accuracy. Evolutionary Computation 3:2 (1995) 149-175.	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.	bib
Hierarchical Decision Rules	Hider-C	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.	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.	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.	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.	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.	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.	bib
Advanced Ant Miner	Advanced_Ant_Miner-C	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.	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.	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.	bib
Advanced Ant Miner+	Advanced_Ant_Miner_Plus-C	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	bib
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.	bib
		H. Ishibuchi, T. Yamamoto. Rule weight specification in fuzzy rule-based classification systems. IEEE Transactions on Fuzzy Systems 13:4 (2005) 428-435.	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.	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.	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.	bib
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.	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.	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.	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.	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.	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.	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.	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.	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.	bib
	MOGUL-C	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.	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.	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.	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.	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.	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.	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.	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.	bib
New SLAVE	NSLV-C	A. González, R. Perez. Improving the genetic algorithm of SLAVE. Mathware and Soft Computing 16 (2009) 59-70.	bib
New SLAVE	NSLV-C	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.	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.	bib
SLAVE2	SLAVE2-C	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.	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.	bib
	SLAVEv0-C	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.	bib
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.	bib
Exemplar-Aided Constructor of Hyperrectangles	EACH-C	S. Salzberg. A Nearest Hyperrectangle Learning Method. Machine Learning 6 (1991) 251-276.	bib
INNER	INNER-C	O. Luaces. Inflating examples to obtain rules. International Journal of Intelligent Systems 18 (2003) 1113-1143.	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.	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.	bib
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.	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.	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.	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.	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.	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.	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.	bib
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.	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.	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.	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.	bib
Functional Trees	FunctionalTrees-C	J. Gama. Functional Trees. Machine Learning 55 (2004) 219-250.	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.	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.	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.	bib
	AdaBoost.NC-C	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.	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.	bib
		J.R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kauffman, 1993.	bib
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.	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.	bib
Multiedit	Multiedit-C	P.A. Devijver. On the editing rate of the MULTIEDIT algorithm. Pattern Recognition Letters 4:1 (1986) 9-12.	bib
Condensed Nearest Neighbor	CNN-C	P.E. Hart. The Condensed Nearest Neighbour Rule. IEEE Transactions on Information Theory 14:5 (1968) 515-516.	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.	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.	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.	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.	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.	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.	bib
Reduced Nearest Neighbor	RNN-C	G.W. Gates. The Reduced Nearest Neighbour Rule. IEEE Transactions on Information Theory 18:3 (1972) 431-433.	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.	bib
Variable Similarity Metric	VSM-C	D.G. Lowe. Similarity Metric Learning For A Variable-Kernel Classifier. Neural Computation 7:1 (1995) 72-85.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	bib
Improved KNN Condensation	IKNN-C	Y. Wu, K. Ianakiev, V. Govindaraju. Improved k-nearest neighbor classification. Pattern Recognition 35:10 (2002) 2311-2318.	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.	bib
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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	bib
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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	bib
Possibilistic instance-based learning	PosIBL-C	E. Hullermeier. Possibilistic instance-based learning. Artificial Intelligence 148 (2003) 335-383.	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.	bib
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.	bib
Learning Vector Quantization 1	LVQ1-C	T. Kohonen. The Self-Organizative Map. Proceedings of the IEEE 78:9 (1990) 1464-1480.	bib
Learning Vector Quantization 2	LVQ2-C	T. Kohonen. The Self-Organizative Map. Proceedings of the IEEE 78:9 (1990) 1464-1480.	bib
Learning Vector Quantization 2.1	LVQ2_1-C	T. Kohonen. The Self-Organizative Map. Proceedings of the IEEE 78:9 (1990) 1464-1480.	bib
Learning Vector Quantization 3	LVQ3-C	T. Kohonen. The Self-Organizative Map. Proceedings of the IEEE 78:9 (1990) 1464-1480.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	bib
Particle Swarm Optimization	PSO-C	L. Nanni, A. Lumini. Particle swarm optimization for prototype reduction. Neurocomputing 72:4-6 (2009) 1092-1097.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	bib
Opposition-Based Differential Evolution	OBDE-C	R.S. Rahnamayan, H.R. Tizhoosh, M.M.A. Salama. Opposition-based differential evolution. IEEE Transactions on Evolutionary Computation 12:1 (2008) 64-79.	bib
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.	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.	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.	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.	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.	bib
Center Nearest Neighbor Classifier	CenterNN	Q. Gao, Z. Wang. Center-based nearest neighbor classifier. Pattern Recognition 40 (2007) 346-349.	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.	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.	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.	bib
Lazy Bayesian Rules classifier	LBR-C	Z. Zheng, G.I. Webb. Lazy Learning of Bayesian Rules. Machine Learning 41 (2000) 53-87.	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.	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.	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.	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.	bib
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.	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.	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.	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.	bib
	MLP-CG-C	B. Widrow, M.A. Lehr. 30 Years of Adaptive Neural Networks: Perceptron, Madaline, and Backpropagation. Proceedings of the IEEE 78:9 (1990) 1415-1442.	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.	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.	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.	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.	bib
Improved Resilient backpropagation Plus	iRProp+-C	C. Igel, M. Husken. Empirical evaluation of the improved Rprop learning algorithm. Neurocomputing 50 (2003) 105-123.	bib
Improved Resilient backpropagation Plus	iRProp+-C	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.	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
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.	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
Genetic Algorithm with Neural Network	GANN-C	X. Yao. Evolving Artificial Neural Networks. Proceedings of the IEEE 87:9 (1999) 1423-1447.	bib
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.	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.	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.	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.	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.	bib
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.	bib
Naïve-Bayes	NB-C	M.E. Maron. Automatic Indexing: An Experimental Inquiry. Journal of the ACM (JACM) 8:3 (1961) 404-417.	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.	bib
		J.H. Friedman. Regularized Discriminant Analysis. Journal of the American Statistical Association 84 (1989) 165-175.	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.	bib
		J.H. Friedman. Regularized Discriminant Analysis. Journal of the American Statistical Association 84 (1989) 165-175.	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.	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.	bib

Regression Algorithms

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.	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.	bib
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.	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.	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.	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.	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.	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.	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.	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.	bib
	GFS-RB-MF-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.	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.	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.	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.	bib
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.	bib
M5	M5-R	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.	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.	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.	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.	bib
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.	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.	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.	bib
Genetic Selection of Linguistic Rule Bases	GFS-RS-T	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	bib
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.	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.	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.	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.	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.	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.	bib
Improved Resilient backpropagation Plus	iRProp+-R	J.H. Wang, Y.W. Yu, J.H. Tsai. On the internal representations of product units. Neural Processing Letters 12:3 (2000) 247-254.	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.	bib
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
Genetic Algorithm with Neural Network	GANN-R	X. Yao. Evolving Artificial Neural Networks. Proceedings of the IEEE 87:9 (1999) 1423-1447.	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.	bib
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.	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.	bib
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.	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.	bib
	GFS-GSP-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.	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.	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.	bib
	GFS-SAP-Sym-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.	bib
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

Imbalanced Classification

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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	bib
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.	bib
Condensed Nearest Neighbor	CNN-I	P.E. Hart. The Condensed Nearest Neighbour Rule. IEEE Transactions on Information Theory 14:5 (1968) 515-516.	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.	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.	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.	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.	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.	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.	bib
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.	bib
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.	bib
C4.5 Cost-Sensitive	C45CS-I	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.	bib
	NNCS-I	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.	bib
C-SVM Cost-Sensitive	C_SVMCS-I	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.	bib
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.	bib
	AdaC2-I	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.	bib
	AdaBoost-I	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.	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.	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.	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.	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.	bib
	Bagging-I	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.	bib
	BalanceCascade-I	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.	bib
	DataBoost-IM-I	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.	bib
	EasyEnsemble-I	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.	bib
		L. Breiman. Pasting small votes for classification in large databases and on-line. Machine Learning 36 (1999) 85-103.	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.	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.	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.	bib
	MSMOTEBoost-I	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.	bib
	OverBagging-I	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.	bib
	OverBagging2-I	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.	bib
	RUSBoost-I	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.	bib
	SMOTEBagging-I	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.	bib
	SMOTEBoost-I	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.	bib
	UnderBagging-I	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.	bib
	UnderBagging2-I	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.	bib
	UnderOverBagging-I	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.	bib
	AdaBoost.NC-I	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.	bib
	EUSBoost-I	J.R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kauffman, 1993.	bib

Semi-Supervised Learning

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.	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.	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.	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.	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.	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.	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.	bib
Co-training with relevant random subspaces	Rel_RASCO	Y. Yaslan, Z. Cataltepe. Co-training with relevant random subspaces. Neurocomputing 73 (2010) 1652-1661.	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.	bib
Democratic co-learning	Democratic	Y. Zhou, S. Goldman. Democratic co-learning. IEEE international conference on tools with artificial intelligence. (2004) 594-602.	bib
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.	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.	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.	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.	bib
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.	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

Subgroup Discovery

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.	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.	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.	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.	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.	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.	bib
MESDIF for Subgroup Discovery	MESDIF-SD	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.	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.	bib

Multi Instance Learning

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.	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.	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.	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.	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.	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.	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.	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.	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.	bib

Clustering Algorithms

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.	bib

Association Rules

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.	bib
Apriori	Apriori-A	C. Borgelt. Efficient implementations of Apriori and Eclat. Workshop of Frequent Item Set Mining Implementations (FIMI 2003). Florida (USA, 2003) 280-296.	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.	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.	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.	bib
Equivalence CLAss Transformation	Eclat-A	C. Borgelt. Efficient implementations of Apriori and Eclat. Workshop of Frequent Item Set Mining Implementations (FIMI 2003). Florida (USA, 2003) 280-296.	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.	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.	bib
Genetic Association Rules	GAR-A	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	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.	bib

Statistical Tests

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.	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.	bib
Wilcoxon signed ranks test (for a single data-set)	Single-Wilcoxon-ST	J.P. Royston. Algorithm AS 181. Applied Statistics 31:2 (1982) 176-180.	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.	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.	bib
Wilcoxon Signed-Rank Test	Wilcoxon-ST	F. Wilcoxon. Individual Comparisons by Ranking Methods. Biometrics 1 (1945) 80-83.	bib
Wilcoxon Signed-Rank Test	Wilcoxon-ST	J.P. Royston. Algorithm AS 181. Applied Statistics 31:2 (1982) 176-180.	bib
Friedman Test and Post-Hoc Procedures	Friedman-ST	D. Sheskin. Handbook of parametric and nonparametric statistical procedures. Chapman and Hall/CRC, 2003.	bib
Friedman Test and Post-Hoc Procedures	Friedman-ST	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.	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.	bib
Quade Test and Post-Hoc Procedures	Quade-ST	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.	bib
Friedman Aligned Test and Post-Hoc Procedures	FriedmanAligned-ST	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.	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.	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.	bib

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.	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.	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.	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.	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.	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.	bib
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.	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