SCI2S Publications (S. García)

Number of Results: 180

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2022 (1)

• [3075] G. González-Almagro, J.L. Suárez, J. Luengo, J.R. Cano, S. García. 3SHACC: Three stages hybrid agglomerative constrained clustering. Neurocomputing 490: 441-461 (2022). doi: 10.1016/j.neucom.2021.12.018
  

2021 (4)

• [3067] N. Rodríguez, D. López, A. Fernández, S. García, F. Herrera. SOUL: Scala Oversampling and Undersampling Library for imbalance classification. SoftwareX 15 (2021) 100767. doi: 10.1016/j.softx.2021.100767
  
• [3077] G. González-Almagro, J. Luengo, J.R. Cano, S. García. Enhancing instance-level constrained clustering through differential evolution. Applied Soft Computing 108, 107435. doi: 10.1016/j.asoc.2021.107435
  
• [3079] M. González, J. Luengo, J. R. Cano, S. García. Synthetic Sample Generation for Label Distribution Learnin. Information Sciences 544: 197-213 (2021). doi: 10.1016/j.ins.2020.07.071
  
• [3081] G. González-Almagro, A. Rosales-Pérez, J. Luengo, J.R. Cano, S. García. ME-MEOA/DCC: Multiobjective constrained clustering through decomposition-based memetic elitism. Swarm Evolutionary Compututation 66: 100939 (2021).
  

2020 (8)

• [2736] A. B. Arrieta, N. Díaz-Rodríguez, J. Del Ser, A. Bennetot, S. Tabik, A. Barbado, S. García, S. Gil-Robles, D. Molina, R. Benjamins, R. Chatila, F. Herrera. Explainable ArtificialIntelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58 (2020) 82-115. doi: 10.1016/j.inffus.2019.12.012
  
• [2790] J. Maillo, S. García, J. Luengo, F. Herrera, I. Triguero. Fast and Scalable Approaches to Accelerate the Fuzzy k Nearest Neighbors Classifier for Big Data. IEEE Transactions on Fuzzy Systems 28(5): 874-886 (2020). doi: 10.1109/TFUZZ.2019.2936356
  
• [2847] D. Molina, J. Poyatos, J.D. Del Ser, S. García, A. Hussain, F. Herrera. Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration Versus Algorithmic Behavior, Critical Analysis Recommendations. Cognitive Computation 12:5 (2020) 897-939. doi: 10.1007/s12559-020-09730-8
  
• [3082] G. González-Almagro, J. Luengo, J. R. Cano, S. García. DILS: Constrained clustering through dual iterative local search. Computers & Operations Research 121: 104979 (2020). doi: 10.1016/j.cor.2020.104979
  
• [3083] J. A. Cortés-Ibáñez, S. González, J. J. Valle-Alonso, J. Luengo, S. García, F. Herrera. Preprocessing methodology for time series: An industrial world application case study. Inf. Sci. 514: 385-401 (2020). doi: j.ins.2019.11.027
  
• [3085] G. González-Almagro, A. Rosales-Pérez, J. Luengo, J.R. Cano, S. García. Improving constrained clustering via decomposition-based multiobjective optimization with memetic elitism. GECCO 2020: 333-341. doi: 10.1145/3377930.3390187
  
• [3086] G. González-Almagro, J.-L. Suárez, J. Luengo, J.R. Cano, S. García. Agglomerative Constrained Clustering Through Similarity and Distance Recalculation. HAIS 2020: 424-436. doi: 10.1007/978-3-030-61705-9_35
  
• [3087] J.R. Cano, J. Luengo, S. García. Similarity-based and Iterative Label Noise Filters for Monotonic Classification. HICSS 2020: 1-9.
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2019 (8)

• [2543] I. Triguero, D. García-Gil, J. Maillo, J. Luengo, S. García, F. Herrera. Transforming big data into smart data: An insight on the use of the k nearest neighbors algorithm to obtain quality data. Wiley Interdisciplinary Reviews. Data Mining and Knowledge Discovery. e1289. doi: 10.1002/widm.1289
  
• [2557] D. García-Gil, J. Luengo, S. García, F. Herrera. Enabling smart data: noise filtering in big data classification. Information Sciences 479, 135-152. doi: 10.1016/j.ins.2018.12.002
  
• [2571] S. González, S. García, S-T. Li, F. Herrera. Chain based sampling for monotonic imbalanced classification. Information Sciences 474 (2019) 187-204. doi: 10.1016/j.ins.2018.09.062
  
• [2667] JR. Cano, J. Luengo, S. García. Label Noise Filtering Techniques to Improve Monotonic Classification. Neurocomputing 353: 83-95 (2019). doi: 10.1016/j.neucom.2018.05.131
  
• [3088] D. García-Gil, F. Luque Sánchez, J. Luengo, S. García, F. Herrera. From Big to Smart Data: Iterative ensemble filter for noise filtering in Big Data classification. International Journal of Intelligent Systems 34(12): 3260-3274 (2019). doi: 10.1002/int.22193
  
• [3089] I. Cordón, J. Luengo, S. García, F. Herrera, F. Charte. Smartdata: Data preprocessing to achieve smart data in R. Neurocomputing 360: 1-13 (2019). doi: 10.1016/j.neucom.2019.06.006
  
• [3091] B. Montesdeoca, J. Luengo, J. Maillo, D. García-Gil, S. García, F. Herrera. A First Approach on Big Data Missing Values Imputation. IoTBDS 2019: 315-323. doi: 10.5220/0007738403150323
  
• [3092] D. García-Gil, A. Alcalde-Barros, J. Luengo, S. García, F. Herrera. Big Data Preprocessing as the Bridge between Big Data and Smart Data: BigDaPSpark and BigDaPFlink Libraries. IoTBDS 2019: 324-331. doi: 10.5220/0007738503240331
  

2018 (23)

• [2319] S. Ramírez-Gallego, S. García, J.M. Benítez, F. Herrera. A distributed evolutionary multivariate discretizer for Big Data processing on Apache Spark. Swarm and Evolutionary Computation 38 (2018) 240-250. doi: 10.1016/j.swevo.2017.08.005
  
• [2596] A. Fernández, S. García, M. Galar, R.C. Prati, B. Krawczyk, F. Herrera. Learning from Imbalanced Data Sets. Springer International Publishing, 2018, ISBN 978-3-319-98073-7. doi: 10.1007/978-3-319-98074-4
  
• [2338] S. Ramírez-Gallego, A. Fernández, S. García, M. Chen, F. Herrera. Big Data: Tutorial and Guidelines on Information and Process Fusion for Analytics Algorithms with MapReduce. Information Fusion 42 (2018) 51-61. doi: 10.1016/j.inffus.2017.10.001
  
• [2364] D. Peralta, I. Triguero, S. García, Y. Saeys, J.M. Benítez, F. Herrera. On the use of convolutional neural networks for robust classiffication of multiple fingerprint captures. International Journal of Intelligent Systems 33:1 (2018) 213–230. doi: 10.1002/int.21948
  
• [2431] A. Fernandez, S. Garcia, N.V. Chawla, F. Herrera. SMOTE for Learning from Imbalanced Data: Progress and Challenges. Marking the 15-year Anniversary. Journal of Artificial Intelligence Research 61 (2018) 863-905. doi: 10.1613/jair.1.11192
  
• [2511] J. Maillo, J. Luengo, S. Garcia, F. Herrera, I. Triguero. A preliminary study on Hybrid Spill-Tree Fuzzy k-Nearest Neighbors for big data classification. 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2018), Rio de Janeiro (Brazil), July 8-13. doi: 10.1109/FUZZ-IEEE.2018.8491595
  
• [2486] I. Cordon, S. Garcia, A. Fernandez, F. Herrera. imbalance: Oversampling Algorithms for Imbalanced Classification in R. Knowledge-Based Systems 161 (2018) 329-341. doi: 10.1016/j.knosys.2018.07.035
  
• [2514] J. Maillo, J. Luengo, S. Garcia, F. Herrera, I. Triguero.. Un enfoque aproximado para acelerar el algoritmo de clasificacion Fuzzy kNN para Big Data. II Workshop en Big Data y Análisis de Datos Escalable (BigDADE 2018), Granada (España), 23-26 octubre 2018.
  
• [2570] Z-L. Zhang, X-G. Luo, S. González, S. García, F. Herrera. DRCW-ASEG: One-versus-One distance-based relative competence weighting with adaptive synthetic example generation for multi-class imbalanced datasets. Neurocomputing 285 (2018) 176-187. doi: 10.1016/j.neucom.2018.01.039
  
• [2572] S. González, S. García, S-T. Li, R. John, F. Herrera. k-Vecinos más Cercanos Difuso para Clasificacion Monotonica. XVIII Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA 2018). IX Simposio de Teoría y Aplicaciones de la Minería de Datos (TAMIDA). Granada (SPAIN), October 23-26, 2018.
  
• [2580] D. García-Gil, S. Ramírez-Gallego, S. García, F. Herrera. Principal Components Analysis Random Discretization Ensemble for Big Data. Knowledge-Based Systems, 150, 2018, 166-174. doi: 10.1016/j.knosys.2018.03.012
  
• [2581] D. García-Gil, S. Ramírez-Gallego, S. García, F. Herrera. On the Use of Random Discretization and Dimensionality Reduction in Ensembles for Big Data. International Conference on Hybrid Artificial Intelligence Systems, 2018, 15-26. doi: 10.1007/978-3-319-92639-1_2
  
• [2583] D. García-Gil, J. Luengo, S. García, F. Herrera. Smart Data: Filtrado de Ruido para Big Data. XVIII Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA2018), II Workshop en Big Data y Análisis de Datos Escalable, Octubre 23-26, 2018.
  
• [2584] D. Charte, F. Charte, S. García, F. Herrera. A snapshot on nonstandard supervised learning problems: taxonomy, relationships, problem transformations and algorithm adaptations. Progress in Artificial Intelligence, 2018, 1-14. doi: 10.1007/s13748-018-00167-7
  
• [2585] S. Ramírez-Gallego, S. García, F. Herrera. Online entropy-based discretization for data streaming classification. Future Generation Computer Systems 86, 2018, 59-70. doi: 10.1016/j.future.2018.03.008
  
• [2586] S. García, ZL. Zhang, A. Altalhi, S. Alshomrani, F. Herrera. Dynamic ensemble selection for multi-class imbalanced datasets. Information Sciences 445, 2018, 22-37. doi: 10.1016/j.ins.2018.03.002
  
• [2587] J.R. Cano, S. García. A First Attempt on Monotonic Training Set Selection. International Conference on Hybrid Artificial Intelligence Systems, 2018, 277-288. doi: 10.1007/978-3-319-92639-1_23
  
• [2589] B. Krawczyk, I. Triguero, S. García, M. Wozniak, F. Herrera. Instance reduction for one-class classification. Knowledge and Information Systems, 2018, 1-28. doi: 10.1007/s10115-018-1220-z
  
• [2590] O. Aoun, A. El Afia, S. García. Self Inertia Weight Adaptation for the Particle Swarm Optimization. Proceedings of the International Conference on Learning and Optimization Algorithms: Theory and Applications, 2018, 8. doi: 10.1145/3230905.3230964
  
• [2591] A. Rosales-Pérez, AE. Gutierrez-Rodríguez, S. García, H. Terashima-Marín, CAC. Coello, F. Herrera. Cooperative multi-objective evolutionary support vector machines for multiclass problems. Proceedings of the Genetic and Evolutionary Computation Conference, 2018, 513-520. doi: 10.1145/3205455.3205524
  
• [2592] A. Rosales-Perez, S. García, H. Terashima-Marin, CAC. Coello, F. Herrera. MC2ESVM: Multiclass Classification Based on Cooperative Evolution of Support Vector Machines. IEEE Computational Intelligence Magazine 13 (2), 2018, 18-29. doi: 10.1109/MCI.2018.2806997
  
• [2593] D. Charte, F. Charte, S. García, M. J. del Jesus, F. Herrera. A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines. Information Fusion 44, 2019, 78–96. doi: 10.1016/j.inffus.2017.12.007
  
• [2601] J. Carrasco, S. García, F. Herrera. shinytests: Una herramienta gráfica para la comparación estadística en minería de datos. XVIII Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA 2018). IX Simposio de Teoría y Aplicaciones de la Minería de Datos (TAMIDA). Granada (SPAIN), October 23-26, 2018.
  

2017 (19)

• [2307] J. Alcalá-Fdez, R. Alcalá, S. González, Y. Nojima, S. García. Evolutionary Fuzzy Rule-Based Methods for Monotonic Classification. IEEE Transactions on Fuzzy Systems 25:6 (2017) 1376-1390. doi: 10.1109/TFUZZ.2017.2718491
  
• [2133] S. García, S. Ramírez-Gallego, J. Luengo, F. Herrera. Big Data: Preprocesamiento y calidad de datos. Novática (Revista de la Asociación de Técnicos de Informática), Monografía Big Data, 237 (2017) 17-23..
  Enlace a la revista completa
  
  
• [2150] Z-L. Zhang, X-G. Luo, S. García, F. Herrera. Cost-Sensitive back-propagation neural networks with binarization techniques in addressing multi-class problems and non-competent classifiers. Applied Soft Computing 56 (2017) 357-367. doi: 10.1016/j.asoc.2017.03.016
  
• [2151] D. Peralta, I. Triguero, S. García, Y. Saeys, J.M. Benítez, F. Herrera. Distributed incremental fingerprint identification with reduced database penetration rate using a hierarchical classification based on feature fusion and selection. Knowledge-Based Systems 126 (2017) 91-103. doi: 10.1016/j.knosys.2017.03.014
  
• [2153] S. Ramírez-Gallego, B. Krawczyk, S. García, M. Wozniak, F. Herrera. A survey on data preprocessing for data stream mining: Current status and future directions. Neurocomputing 239 (2017) 39-57. doi: 10.1016/j.neucom.2017.01.078
  
• [2154] D. Peralta, S. García, J.M. Benítez, F. Herrera. Minutiae-Based Fingerprint Matching Decomposition: Methodology for Big Data Frameworks. Information Sciences 408 (2017) 198-212. doi: 10.1016/j.ins.2017.05.001
  
• [2155] J.R. Cano, N.R. Aljohani, R.A. Abbasi, J.S. Alowidbi, S. García. Prototype selection to improve monotonic nearest neighbor. Engineering Applications of Artificial Intelligence 60 (2017) 128-135. doi: 10.1016/j.engappai.2017.02.006
  
• [2156] S. González, S. García, M. Lázaro, A.. Figueiras-Vidal, F. Herrera. Class Switching according to Nearest Enemy Distance for learning from highly imbalanced data-sets. Pattern Recognition 70 (2017) 12-24. doi: 10.1016/j.patcog.2017.04.028
  
• [2157] M.A. Jarwar, R.A. Abbasi, M. Mushtaq, O. Maqbool, N.R. Aljohani, A. Daud, J.S. Alowibdi, J.R. Cano, S. García, I. Chong. CommuniMents: A Framework for Detecting Community Based Sentiments for Events. International Journal on Semantic Web and Information Systems (IJSWIS) 13:2 (2017) 87-108. doi: 10.4018/IJSWIS.2017040106
  
• [2158] Z-L. Zhang, X-G. Luo, S. García, J-F. Tang, F. Herrera. Exploring the effectiveness of dynamic ensemble selection in the one-versus-one scheme. Knowledge-Based Systems 125 (2017) 53-63. doi: 10.1016/j.knosys.2017.03.026
  
• [2159] A. Rosales-Perez, S. García, J.A. Gonzalez, C.A.C. Coello, F. Herrera. An Evolutionary Multi-Objective Model and Instance Selection for Support Vector Machines with Pareto-based Ensembles. IEEE Transactions on Evolutionary Computation (Volume: 21, Issue: 6, Dec. 2017) 863-877. doi: 10.1109/TEVC.2017.2688863
  
• [2160] D. García-Gil, S. Ramírez-Gallego, S. García, F. Herrera. A comparison on scalability for batch big data processing on Apache Spark and Apache Flink. Big Data Analytics 2:1 (2017) 1. doi: 10.1186/s41044-016-0020-2
  
• [2170] S. Ramírez-Gallego, B. Krawczyk, S. García, M. Wozniak, J.M. Benítez, F. Herrera. Nearest Neighbor Classification for High-Speed Big Data Streams Using Spark. IEEE Transactions on Systems, Man, and Cybernetics: Systems 47:10 (2017) 2727-2739. doi: 10.1109/TSMC.2017.2700889
  
• [2180] J. García, H.M. Fardoun, D.M. Alghazzawi, J.R. Cano, S. García. MoNGEL: monotonic nested generalized exemplar learning. Pattern Analysis and Applications 20:2 (2017) 441–452. doi: 10.1007/s10044-015-0506-y
  
• [2310] J. Maillo, J. Luengo, S. Garcia, F. Herrera, I. Triguero. Exact Fuzzy k-Nearest Neighbor Classification for Big Datasets. IEEE Conference on Fuzzy Systems (FUZZ-IEEE 2017), Naples (Italy), July 9-12.
  
• [2322] I. Triguero, S. González, J.M. Moyano, S. García, J. Alcalá-Fdez, J. Luengo, A. Fernández, M.J. del Jesús, L. Sánchez and F. Herrera. KEEL 3.0: An Open Source Software for Multi-Stage Analysis in Data Mining. International Journal of Computational Intelligence Systems 10 (2017) 1238-1249.
  
• [2368] J. R. Cano, S. García. Training Set Selection for Monotonic Ordinal Classification. Data & Knowledge Engineering 112 (2017) 94-105. doi: 10.1016/j.datak.2017.10.003
  
• [2594] J. Carrasco, S. García, M. M. Rueda, F. Herrera. rNPBST: An R Package Covering Non-parametric and Bayesian Statistical Tests. 12th International Conference on Hybrid Artificial Intelligent Systems (HAIS 2017), Lecture Notes in Computer Science (LNCS 10334) 281-292, Logroño, Spain, June 21-23, 2017. doi: 10.1007/978-3-319-59650-1_24
  
• [2595] S. Ramírez-Gallego, B. Krawczyk, S. García, M. Wozniak, J.M. Benítez, F. Herrera. Fast Case-Based Reasoning for Large-Scale Streaming Classification. 8th International Conference of Pattern Recognition Systems (ICPRS 2017), Madrid (Spain), July 11-13, 2017. doi: 10.1049/cp.2017.0150
  

2016 (16)

• [1896] S. Ramírez-Gallego, S. García, J.M. Benítez, F. Herrera. Multivariate Discretization Based on Evolutionary Cut Points Selection for Classification. IEEE Transaction on Cybernetics 46:3 (2016) 595-608. doi: 10.1109/TCYB.2015.2410143
  
• [1964] J. Derrac, F. Chiclana, S. García, F. Herrera. Evolutionary fuzzy k-nearest neighbors algorithm using interval-valued fuzzy sets. Information Sciences 329 (2016) 144-163. doi: 10.1016/j.ins.2015.09.007
  
• [1968] N. Verbiest, J. Derrac, C. Cornelis, S. García, F. Herrera. Evolutionary Wrapper Approaches for Training Set Selection as Preprocessing Mechanism for Support Vector Machines: Experimental Evaluation and Support Vector Analysis. Applied Soft Computing 38 (2016) 10-22. doi: 10.1016/j.asoc.2015.09.006
  
• [1996] S. Ramírez-Gallego, S. García, H. Mouriño Talín, D. Martínez-Rego, V. Bolón-Canedo, A. Alonso-Betanzos, J. M. Benítez, F. Herrera. Data discretization: taxonomy and big data challenge. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 6:1 (2016) 5-21. doi: 10.1002/widm.1173
  
• [2014] S. García, J. Luengo, F. Herrera. Tutorial on practical tips of the most influential data preprocessing algorithms in data mining. Knowledge-Based Systems 98 (2016) 1–29. doi: 10.1016/j.knosys.2015.12.006
  COMPLEMENTARY MATERIAL to the paper: datasets, experimental results and source codes
  
  
• [2075] D. Peralta, I. Triguero, S. García, F. Herrera, J.M. Benítez. DPD-DFF: A Dual Phase Distributed Scheme with Double Fingerprint Fusion for Fast and Accurate Identification in Large Databases. Information Fusion 32 (2016) 40–51. doi: 10.1016/j.inffus.2016.03.002
  
• [2138] I. Triguero, J. Maillo, J. Luengo, S. García, F. Herrera. From Big data to Smart Data with the K-Nearest Neighbours algorithm. The 2016 IEEE International Conference on Smart Data (SmartData 2016), Chengdu (China), Dec 16-19, 2016. doi: 10.1109/iThings-GreenCom-CPSCom-SmartData.2016.177
  
• [2161] S. Gutiérrez, S. García. Landmark-based music recognition system optimisation using genetic algorithms. Multimedia Tools and Applications 75:24 (2016) 16905-16922. doi: 10.1007/s11042-015-2963-0
  
• [2162] S. García, S. Ramírez-Gallego, J. Luengo, J.M. Benítez, F. Herrera. Big data preprocessing: methods and prospects. Big Data Analytics 1:9 (2016). doi: 10.1186/s41044-016-0014-0
  
• [2164] Z-L. Zhang, B. Krawczyk, S. García, A. Rosales-Pérez, F. Herrera. Empowering one-vs-one decomposition with ensemble learning for multi-class imbalanced data. Knowledge-Based Systems 106 (2016) 251-263. doi: 10.1016/j.knosys.2016.05.048
  
• [2165] P.A. Gutiérrez, S. García. Current prospects on ordinal and monotonic classification. Progress in Artificial Intelligence 5:3 (2016), 171-179. doi: 10.1007/s13748-016-0088-y
  
• [2172] J. García, J.R. Cano, S. García. A Nearest Hyperrectangle Monotonic Learning Method. 11th International Conference on Hybrid Artificial Intelligence Systems (HAIS 2016). Lecture Notes in Computer Science book series (LNCS) 9648, Seville (Spain) 311-322. doi: 10.1007/978-3-319-32034-2_26
  
• [2173] S. González, F. Herrera, S. García. Managing Monotonicity in Classification by a Pruned AdaBoost. 11th International Conference on Hybrid Artificial Intelligence Systems (HAIS 2016). Lecture Notes in Computer Science book series (LNCS) 9648, Seville (Spain) 512-523. doi: 10.1007/978-3-319-32034-2_43
  
• [2176] J. García, A.M. AlBar, N.R. Aljohani, J.R. Cano, S. García. Hyperrectangles selection for monotonic classification by using evolutionary algorithms. International Journal of Computational Intelligence Systems 9:1 (2016), 184-201. doi: 10.1080/18756891.2016.1146536
  
• [2178] S. Ramírez-Gallego, S. García, J.M. Benítez, F. Herrera. Discretización Multivariada basada en Selección de Puntos Evolutiva para Clasificación. XVII Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA), XI Symposio de Metaheurísticas, Algoritmos Evolutivos y Bioinspirados (MAEB), September 14-16, 2016.
  
• [2191] S. González, M. Lázaro, A.R. Figueiras-Vidal, S. García, F. Herrera. Intercambio de Clases de acuerdo a la Distancia al Enemigo más Cercano para Problemas con Clases altamente No Balanceadas. XVII Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA), III Jornadas de Fusión de la Información y ensembles (FINO), Salamanca (Spain), September 14-16, 2016.
  

2015 (14)

• [1683] I. Triguero, S. García, F. Herrera. Self-Labeled Techniques for Semi-Supervised Learning: Taxonomy, Software and Empirical Study. Knowledge and Information Systems 42 (2015) 245-284. doi: 10.1007/s10115-013-0706-y
  COMPLEMENTARY MATERIAL to the paper: datasets, experimental results and source codes
  
  
• [1769] I. Triguero, D. Peralta, J. Bacardit, S. García, F. Herrera. MRPR: A MapReduce Solution for Prototype Reduction in Big Data Classification. Neurocomputing 150 (2015), 331-345. doi: 10.1016/j.neucom.2014.04.078
  
• [1788] I. Triguero, S. García, F. Herrera. SEG-SSC: A Framework based on Synthetic Examples Generation for Self-Labeled Semi-Supervised Classification. IEEE Transactions on Cybernetics 45:4 (2015) 622-634. doi: 10.1109/TCYB.2014.2332003
  
• [1890] M. Galar, J. Derrac, D. Peralta, I. Triguero, D. Paternain, C. Lopez-Molina, S. García, J.M. Benítez, M. Pagola, E. Barrenechea, H. Bustince, F. Herrera. A Survey of Fingerprint Classification Part I: Taxonomies on Feature Extraction Methods and Learning Models. Knowledge-Based Systems 81 (2015) 76-97. doi: 10.1016/j.knosys.2015.02.008
  
• [1891] M. Galar, J. Derrac, D. Peralta, I. Triguero, D. Paternain, C. Lopez-Molina, S. García, J.M. Benítez, M. Pagola, E. Barrenechea, H. Bustince, F. Herrera. A Survey of Fingerprint Classification Part II: Experimental Analysis and Ensemble Proposal. Knowledge-Based Systems 81 (2015) 98-116. doi: 10.1016/j.knosys.2015.02.015
  COMPLEMENTARY MATERIAL to the paper: datasets, experimental results and source codes
  
  
• [1921] D. Peralta, M. Galar, I. Triguero, D. Paternain, S. García, E. Barrenechea, J. M. Benítez, H. Bustince, F. Herrera. A Survey on Fingerprint Minutiae-based Local Matching for Verification and Identification: Taxonomy and Experimental Evaluation. Information Sciences 315 (2015) 67-87. doi: 10.1016/j.ins.2015.04.013
  COMPLEMENTARY MATERIAL to the paper: experimental results and statistical tests
  
  
• [1980] S. González, F. Herrera, S. García. Monotonic Random Forest with an Ensemble Pruning Mechanism based on the Degree of Monotonicity. New Generation Computing 33:4 (2015) 367-388. doi: 10.1007/s00354-015-0402-4
  
• [1989] D. Peralta, I. Triguero, Y. Saeys, S. García, J.M. Benítez, F. Herrera. Clasificación Jerárquica de Huellas Dactilares con Selección de Características. VII Symposium of Theory and Applications of Data Mining (TAMIDA), CAEPIA 2015, Albacete (España), pp. 831-840, 09-12 Noviembre 2015.
  
• [2057] S. González, F. Herrera, S. García. Managing Monotonicity in Classification by a Pruned Random Forest. Intelligent Data Engineering and Automated Learning (IDEAL 2015), Lecture Notes in Computer Science (LNCS) 9375, 53-60, 2015. doi: 10.1007/978-3-319-24834-9_7
  
• [2060] S. García, J. Luengo, F. Herrera. Data Preprocessing in Data Mining. Intelligent Systems Reference Library. Series Editors: Kacprzyk, Janusz, Jain, Lakhmi C. ISSN: 1868-4394.
  
• [2063] S. Ramírez-Gallego, S. Garcia, H. Mourino-Talin, D. Martinez-Rego, V. Bolon-Canedo, A. Alonso-Betanzos, J.M. Benítez, F. Herrera. Distributed Entropy Minimization Discretizer for Big Data Analysis under Apache Spark. 9th International Conference on Big Data Science and Engineering (IEEE BigDataSE-15), Helsinki (Finland), 33-40, August 20-22, 2015. doi: 10.1109/Trustcom.2015.559
  
• [2065] J. Derrac, F. Chiclana, S. García, F. Herrera. An Interval Valued K-Nearest Neighbors Classifier. Proceedings of the International Joint Conference IFSA-EUSFLAT-2015, Gijón, Asturias (Spain). doi: doi:10.2991/ifsa-eusflat-15.2015.55
  
• [2168] S. Ramírez-Gallego, S. García, J.M. Benítez, F. Herrera. A Wrapper Evolutionary Approach for Supervised Multivariate Discretization: A Case Study on Decision Trees. A Wrapper Evolutionary Approach for Supervised Multivariate Discretization: A Case Study on Decision Trees. In: Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015. Advances in Intelligent Systems and Computing, vol 403. Springer, Cham. doi: 10.1007/978-3-319-26227-7_5
  
• [2192] S. González, F. Herrera, S. García. Clasificación Monotónica mediante poda de Bosques Aleatorios. XVI Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA), Albacete (Spain), November 9-12, 2015.
  

2014 (8)

• [1698] J. Derrac, S. García, F. Herrera. Fuzzy Nearest Neighbor Algorithms: Taxonomy, Experimental analysis and Prospects . Information Sciences 260 (2014) 98-119. doi: 10.1016/j.ins.2013.10.038
  COMPLEMENTARY MATERIAL to the paper: datasets, experimental results and source codes
  
  
• [1588] V. López, I. Triguero, C.J. Carmona, S. García, F. Herrera. Addressing Imbalanced Classification with Instance Generation Techniques: IPADE-ID. Neurocomputing 126 (2014) 15-28. doi: 10.1016/j.neucom.2013.01.050
  
• [1646] I. Triguero, José A. Sáez, J. Luengo, S. García, F. Herrera. On the Characterization of Noise Filters for Self-Training Semi-Supervised in Nearest Neighbor Classification. Neurocomputing 132 (2014) 30-41. doi: 10.1016/j.neucom.2013.05.055
  
• [1794] J. Derrac, S. García, S. Hui, P. N. Suganthan, F. Herrera. Analyzing convergence performance of evolutionary algorithms: A statistical approach. Information Sciences 289 (2014) 41-58. doi: 10.1016/j.ins.2014.06.009
  COMPLEMENTARY MATERIAL to the paper
  
  
• [1806] I. Triguero, D. Peralta, J. Bacardit, S. García, F. Herrera. A Combined MapReduce-Windowing Two-Level Parallel Scheme for Evolutionary Prototype Generation. In Proceeding on the WCCI 2014 IEEE World Congress on Computational Intelligence, IEEE Congress on Evolutionary Computation CEC'2014, Beijing (China), 6-11 July, pp. 3036-3043, 2014. doi: 10.1109/CEC.2014.6900490
  
• [1807] B. Krawczyk, I. Triguero, S. García, M. Wozniak, F. Herrera. A First Attempt on Evolutionary Prototype Reduction for Nearest Neighbor One-Class Classification.. In Proceeding on the WCCI 2014 IEEE World Congress on Computational Intelligence, IEEE Congress on Evolutionary Computation CEC'2014, Beijing (China), 6-11 July, pp 747-753, 2014.
  
• [2181] I. Triguero, S. García, F. Herrera, Y. Saeys. Improving disease prediction using unlabeled and synthetic samples. 9th Benelux Bioinformatics Conference, Bioinformatics: Integrating data, teams and disciplines. Novotel-Kirchberg (Luxembourg), December 8-9, 2014.
  
• [2432] S. García, J. Derrac, S. Ramírez-Gallego, F. Herrera. On the statistical analysis of the parameters’ trend in a machine learning algorithm. Progress in Artificial Intelligence 3:1 (2014) 51-53. doi: 10.1007/s13748-014-0043-8
  

2013 (5)

• [1700] I. Triguero, S. García, F. Herrera. Tri-Training con sobremuestreo para aprendizaje semi-supervisado. Actas de la XV Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA 2013), Madrid (Spain), pp 149-158 , 17-20 Septiembre, 2013.
  
• [1469] S. García, J. Luengo, José A. Sáez, V. López, F. Herrera. A Survey of Discretization Techniques: Taxonomy and Empirical Analysis in Supervised Learning. IEEE Transactions on Knowledge and Data Engineering 25:4 (2013) 734-750. doi: 10.1109/TKDE.2012.35
  COMPLEMENTARY MATERIAL to the paper
  
  
• [1537] 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. doi: 10.1007/s00500-012-0888-3
  
• [1657] V. López, A. Fernandez, S. García, V. Palade, F. Herrera. An Insight into Classification with Imbalanced Data: Empirical Results and Current Trends on Using Data Intrinsic Characteristics. Information Sciences 250 (2013) 113-141. doi: 10.1016/j.ins.2013.07.007
  COMPLEMENTARY MATERIAL to the paper
  
  
• [1675] J. Derrac, S. García, Sheldon Hui , F. Herrera, Ponnuthurai N. Suganthan. Statistical Analysis of Convergence Performance Throughout the Evolutionary Search: A Case Study with SaDE-MMTS and Sa-EPSDE-MMTS. 2013 IEEE Symposium on Differential Evolution (SDE), Singapore, pp. 151-156, April 16-19, 2013.
  

2012 (13)

• [1365] I. Triguero, J. Derrac, S. García, F. Herrera. A Taxonomy and Experimental Study on Prototype Generation for Nearest Neighbor Classification. IEEE Transactions on Systems, Man, and Cybernetics--Part C: Applications and Reviews 42 (1) (2012) 86-100. doi: 10.1109/TSMCC.2010.2103939
  COMPLEMENTARY MATERIAL to the paper: datasets, experimental results and source codes
  
  
• [1372] S. García, J. Derrac, I. Triguero, C.J. Carmona, F. Herrera. Evolutionary-Based Selection of Generalized Instances for Imbalanced Classification. Knowledge Based Systems 25:1 (2012) 3-12. doi: 10.1016/j.knosys.2011.01.012
  
• [1408] J. Luengo, S. García, F. Herrera. On the choice of the best imputation methods for missing values considering three groups of classification methods. Knowledge and Information Systems 32:1 (2012) 77-108. doi: 10.1007/s10115-011-0424-2
  COMPLEMENTARY MATERIAL to the paper: Software, data sets, results and methods description
  
  
• [1409] S. García, J. Derrac, J.R. Cano, F. Herrera. Prototype Selection for Nearest Neighbor Classification: Taxonomy and Empirical Study. IEEE Transactions on Pattern Analysis and Machine Intelligence 34:3 (2012) 417-435. doi: 10.1109/TPAMI.2011.142
  COMPLEMENTARY MATERIAL to the paper: datasets, experimental results and source codes
  
  
• [1428] 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. doi: 10.1016/j.ins.2011.09.027
  
• [1451] J. Derrac, J. Luengo, A. Fernandez, S. García, J. Alcalá-Fdez. KEEL: Una herramienta docente para sistemas difusos. XVI Congreso Español sobre Tecnologías y Lógica Fuzzy (ESTYLF 2012), Valladolid (España), pp. 534-539, 1-3 Febrero.
  
• [1504] J. Derrac, S. García, D. Molina, F. Herrera. Un tutorial sobre el uso de test estadísticos no paramétricos en comparaciones múltiples de metaheurísticas y algoritmos evolutivos. Actas del VIII Congreso Español sobre Metaheurísticas, Algoritmos Evolutivos y Bioinspirados (MAEB12), Albacete (Spain), pp. 455-462, Febrero 8-10, 2012.
  
• [1502] I. Triguero, J. Derrac, S. García, F. Herrera. Evolución Diferencial para Reducción de Prototipos y Ponderación de Características. Actas del VIII Congreso Español sobre Metaheurísticas, Algoritmos Evolutivos y Bioinspirados (MAEB12), Albacete (Spain), pp. 267-274, Febrero 8-10, 2012.
  
• [1505] S. García, V. López, J. Luengo, C.J. Carmona, F. Herrera. A Preliminary Study on Selecting the Optimal Cut Points in Discretization by Evolutionary Algorithms. 1st International Conference on Pattern Recognition Applications and Methods (ICPRAM2012), Vilamoura (Portugal), pp. 211-216, 6-8 February 2012.
  
• [1514] 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. doi: 10.1109/TSMCB.2012.2191953
  COMPLEMENTARY MATERIAL to the paper: datasets, experimental results and source codes
  
  
• [1516] J. Derrac, I. Triguero, S. García, F. Herrera. A Co-evolutionary Framework for Nearest Neighbor Enhancement: Combining Instance and Feature Weighting with Instance Selection. In Proceedings of the Seventh International Conference on Hybrid Artificial Intelligence Systems (HAIS 2012), March 28-30, Salamanca (Spain), Lecture Notes in Computer Science 7209, 176-187.
  
• [1530] I. Triguero, J. Derrac, S. García, F. Herrera. Integrating a Differential Evolution Feature Weighting scheme into Prototype Generation. Neurocomputing 97 (2012) 332-343. doi: 10.1016/j.neucom.2012.06.009
  
• [1710] C.J. Carmona, S. Ramírez-Gallego, F. Torres, E. Bernal, M.J. del Jesus, S. García. Web usage mining to improve the design of an e-commerce website: OrOliveSur.com . Expert Systems with Applications 39 (2012) 11243-11249. doi: 10.1016/j.eswa.2012.03.046
  

2011 (12)

• [1276] J. Luengo, A. Fernandez, S. García, F. Herrera. Addressing Data Complexity for Imbalanced Data Sets: Analysis of SMOTE-based Oversampling and Evolutionary Undersampling. Soft Computing, 15 (10) (2011) 1909-1936. doi: 10.1007/s00500-010-0625-8
  
• [1277] J. Alcalá-Fdez, A. Fernandez, J. Luengo, J. Derrac, S. García, L. Sánchez, F. Herrera. KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework. Journal of Multiple-Valued Logic and Soft Computing 17:2-3 (2011) 255-287.
  SOFTWARE associated to the paper here
  
  
• [1327] 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. doi: 10.1016/j.patcog.2010.10.020
  
• [1342] 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. doi: 10.1016/j.asoc.2010.11.030
  
• [1374] J. Derrac, S. García, D. Molina, F. Herrera. A Practical Tutorial on the Use of Nonparametric Statistical Tests as a Methodology for Comparing Evolutionary and Swarm Intelligence Algorithms. Swarm and Evolutionary Computation 1:1 (2011) 3-18. doi: 10.1016/j.swevo.2011.02.002
  COMPLEMENTARY MATERIAL to the paper: Software and tests description
  
  
• [1385] A. Fernandez, S. García, F. Herrera. Addressing the Classication with Imbalanced Data: Open Problems and New Challenges on Class Distribution. 6th International Conference on Hybrid Artificial Intelligence Systems (HAIS2011). Wroclaw, Poland, 23-25 May 2011, pp. 1-10.
  
• [1407] I. Triguero, S. García, F. Herrera. Enhancing IPADE Algorithm with a Different Individual Codification. 6th International Conference on Hybrid Artificial Intelligence Systems (HAIS2011). Wroclaw, Poland, 23-25 May 2011, LNAI 6679, pp. 262–270.
  
• [1413] J. Derrac, C. Cornelis, S. García, F. Herrera. A Preliminary Study on the Use of Fuzzy Rough Set Based Feature Selection for Improving Evolutionary Instance Selection Algorithms. 11th International Work-Conference on Artificial Neural Networks (IWANN'11), Lecture Notes on Computer Science 6691, Torremolinos (Spain), pp. 174-182, June 8-10, 2011.
  
• [1439] I. Triguero, J. Derrac, S. García, F. Herrera. A Study of the Scaling up Capabilities of Stratified Prototype Generation. Third World Congress on Nature and Biologically Inspired Computing (NABIC'11), Salamanca (Spain), pp. 304-309, October 19-21, 2011.
  
• [1440] J. Derrac, J. Luengo, J. Alcalá-Fdez, A. Fernandez, S. García. Using KEEL Software as a Educational Tool: A Case of Study Teaching Data Mining. Second International Conference on EUropean Transnational Education (ICEUTE 2011), Salamanca (Spain), pp. 55-60, October 20-21, 2011.
  
• [1444] I. Triguero, J. Derrac, S. García, F. Herrera. Un esquema de Pesos basado en Evolución Diferencial para Generación de Prototipos. Actas de la XIV Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA11), Tenerife (Spain), November 7-11, 2011.
  
• [1445] J. Derrac, S. García, C. Cornelis, F. Herrera. Una aplicación de conjuntos rugosos difusos en selección de características para la mejora de métodos de selección de instancias evolutivos. Actas de la XIV Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA11), Tenerife (Spain), November 7-11, 2011.
  

2010 (15)

• [1226] 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. doi: 10.1016/j.patcog.2009.12.012
  
• [1051] J. Alcalá-Fdez, S. García, L. Sánchez, I. Robles, M.J. del Jesus, E. Bernadó-Mansilla, A. Peregrin, F. Herrera. Introduction to the Experimental Design in the Data Mining Tool KEEL. L.S.L. Wang , T.P. Hong (Eds.) Intelligent Soft Computation and Evolving Data Mining: Integrating Advanced Technology, IGI Global, 2010, 1-25. ISBN 978-1-61520-757-2.
  
• [1072] S. García, J.R. Cano, F. Herrera. A Review on Evolutionary Prototype Selection: An Empirical Study of Performance and Efficiency. In: Chiong, R. (eds.): Intelligent Systems for Automated Learning and Adaptation: Emerging Trends and Applications. IGI Global (2010) 92-113.
  
• [1100] J. Derrac, S. García, F. Herrera. A Survey on Evolutionary Instance Selection and Generation. International Journal of Applied Metaheuristic Computing 1:1 (2010) 60-92. doi: 10.4018/IJAMC.2010010104
  
• [1104] A. Fernandez, S. García, J. Luengo, E. Bernadó-Mansilla, F. Herrera. Genetics-Based Machine Learning for Rule Induction: State of the Art, Taxonomy and Comparative Study. IEEE Transactions on Evolutionary Computation 14:6 (2010) 913-941. doi: 10.1109/TEVC.2009.2039140
  COMPLEMENTARY MATERIAL to the paper: dataset partitions, results, figures, etc
  
  
• [1112] J. Luengo, S. García, F. Herrera. A Study on the Use of Imputation Methods for Experimentation with Radial Basis Function Network Classifiers Handling Missing Attribute Values: The good synergy between RBFs and EventCovering method. Neural Networks 23 406-418. doi: 10.1016/j.neunet.2009.11.014
  COMPLEMENTARY MATERIAL to the paper: dataset partitions, results, figures, etc
  
  
• [1206] S. García, A. Fernandez, J. Luengo, F. Herrera. Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental Analysis of Power. Information Sciences 180 (2010) 2044–2064. doi: 10.1016/j.ins.2009.12.010
  COMPLEMENTARY MATERIAL to the paper: Software and tests description
  
  
• [1250] J. Derrac, A. Fernandez, J. Luengo, S. García, L. Sánchez, J. Alcalá-Fdez, F. Herrera. KEEL: Una herramienta software para el análisis de sistemas difusos evolutivos. XV Congreso Español sobre Tecnologías y Lógica Fuzzy (ESTYLF 2010), Huelva (Spain), 417-422, 3-5 February 2010..
  
• [1275] S. García, J. Derrac, I. Triguero, C. Carmona, F. Herrera. A Preliminary Study on the Selection of Generalized Instances for Imbalanced Classification. Twenty Third International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems (IEA/AIE 2010), Córdoba (Spain), Lecture Notes in Artificial Intelligence (LNAI) 6096, 601-610, , 1-4 June 2010.
  
• [1285] J. Derrac, S. García, F. Herrera. Stratified Prototype Selection based on a Steady-State Memetic Algorithm: A Study of scalability. Memetic Computing 2:3 (2010) 183-199. doi: 10.1007/s12293-010-0048-1
  
• [1291] I. Triguero, S. García, F. Herrera. A preliminary study on the use of differential evolution for adjusting the position of examples in nearest neighbor classification. In Proceeding on the WCCI 2010 IEEE World Congress on Computational Intelligence, IEEE Congress on Evolutionary Computation CEC'2010, Barcelona (Spain), 18-23 July, pp 630-637, 2010.
  
• [1306] J. Derrac, I. Triguero, S. García, F. Herrera. Coevolución de selección de instancias y esquemas de pesos para clasificadores basados en la regla del vecino más cercano. In Proceedings of the III Congreso Español de Informática (CEDI 2010). VII Congreso Español sobre Metaheurísticas, Algoritmos Evolutivos y Bioinspirados, MAEB2010, Valencia (Spain), 481-488, 7-10 September 2010.
  
• [1307] S. García, J. Derrac, F. Herrera. Un Tutorial Metodológico para hacer Comparaciones Estadísticas con Tests No Paramétricos en Propuestas de Minería de Datos. In Proceedings of the III Congreso Español de Informática (CEDI 2010). V Simposio de Teoría y Aplicaciones de Minería de Datos, TAMIDA2010, Valencia (Spain), 155-164, 7-10 September 2010.
  
• [1316] I. Triguero, S. García, F. Herrera. IPADE: Iterative Prototype Adjustment for Nearest Neighbor Classification. IEEE Transactions on Neural Networks 21 (12) (2010) 1984-1990. doi: 10.1109/TNN.2010.2087415
  COMPLEMENTARY MATERIAL to the paper: datasets, experimental results and source codes
  
  
• [1339] J. Derrac, S. García, F. Herrera. IFS-CoCo in the Landscape Contest: Description and results. Proceedings of Contests of the Twentieth conference of the International Association for Pattern Recognition (ICPR'2010), LNCS vol. 6388, Springer, Heidelberg. Estambul (Turkey, 2010) .
  

2009 (11)

• [0758] J. Alcalá-Fdez, L. Sánchez, S. García, M.J. del Jesus, S. Ventura, J.M. Garrell, J. Otero, C. Romero, J. Bacardit, V.M. Rivas, J.C. Fernández, F. Herrera. KEEL: A Software Tool to Assess Evolutionary Algorithms for Data Mining Problems. Soft Computing 13:3 (2009) 307-318. doi: 10.1007/s00500-008-0323-y
  SOFTWARE associated to the paper
  
  
• [0834] S. García, D. Molina, M. Lozano, F. Herrera. A Study on the Use of Non-Parametric Tests for Analyzing the Evolutionary Algorithms' Behaviour: A Case Study on the CEC'2005 Special Session on Real Parameter Optimization. Journal of Heuristics 15 (2009) 617-644. doi: 10.1007/s10732-008-9080-4
  COMPLEMENTARY MATERIAL to the paper: Software and tests description
  
  
• [1034] J. Derrac, S. García, F. Herrera. A First Study on the Use of Coevolutionary Algorithms for Instance and Feature Selection. In Proceedings of the Fourth International Conference on Hybrid Artificial Intelligence Systems (HAIS 2009), June 10-12, Salamanca (Spain), Lecture Notes in Artificial Intelligence 5572, 557-564.
  
• [0826] S. García, F. Herrera. Evolutionary Under-Sampling for Classification with Imbalanced Data Sets: Proposals and Taxonomy. Evolutionary Computation 17:3 (2009) 275-306.
  
• [0875] S. García, J.R. Cano, E. Bernadó-Mansilla, F. Herrera. Diagnose of Effective Evolutionary Prototype Selection using an Overlapping Measure. International Journal of Pattern Recognition and Artificial Intelligence 23:8 (2009) 1527-1548.
  
• [0893] J. Luengo, S. García, F. Herrera. A Study on the Use of Statistical Tests for Experimentation with Neural Networks: Analysis of Parametric Test Conditions and Non-Parametric Tests. Expert Systems with Applications 36 (2009) 7798-7808. doi: 10.1016/j.eswa.2008.11.041
  COMPLEMENTARY MATERIAL to the paper: Software and tests description
  
  
• [0898] S. García, A. Fernandez, J. Luengo, F. Herrera. A Study of Statistical Techniques and Performance Measures for Genetics-Based Machine Learning: Accuracy and Interpretability. Soft Computing 13:10 (2009) 959-977. doi: 10.1007/s00500-008-0392-y
  COMPLEMENTARY MATERIAL to the paper: Software and tests description
  
  
• [1025] S. García, A. Fernandez, F. Herrera. Un Primer Estudio sobre la Utilización de Selección Evolutiva de Conjuntos de Entrenamiento en Problemas de Clasificación con Clases no Balanceadas y Árboles de Decisión. In Proceedings of VI Congreso Español sobre Metaheurísticas, Algoritmos Evolutivos y Bioinspirados (MAEB'09), Málaga (Spain), 183-190, 11-13 February 2009.
  
• [1047] S. García, A. Fernandez, F. Herrera. Enhancing the Effectiveness and Interpretability of Decision Tree and Rule Induction Classifiers with Evolutionary Training Set Selection over Imbalanced Problems. Applied Soft Computing 9 (2009) 1304-1314. doi: 10.1016/j.asoc.2009.04.004
  
• [1176] J. Luengo, A. Fernandez, F. Herrera, S. García. Addressing Data-Complexity for Imbalanced Data-sets: A Preliminary Study on the Use of Preprocessing for C4.5. 9th International Conference on Intelligent Systems Designs and Applications (ISDA'09), Pisa (Italy) November 2009, 523-528 .
  
• [1177] S. García, J. Derrac, J. Luengo, F. Herrera. A First Approach to Nearest Hyperrectangle Selection by Evolutionary Algorithms. 9th International Conference on Intelligent Systems Designs and Applications (ISDA'09), Pisa (Italy) November 2009, 517-522.
  

2008 (10)

• [0721] J.R. Cano, F. Herrera, M. Lozano, S. García. Making CN2-SD Subgroup Discovery Algorithm scalable to Large Size Data Sets using Instance Selection. Expert Systems with Applications 35 (2008) 1949-1965. doi: 10.1016/j.eswa.2007.08.083
  
• [0772] A. Fernandez, S. García, M.J. del Jesus, F. Herrera. A Study of the Behaviour of Linguistic Fuzzy Rule Based Classification Systems in the Framework of Imbalanced Data Sets. Fuzzy Sets and Systems, 159:18 (2008) 2378-2398. doi: 10.1016/j.fss.2007.12.023
  
• [0773] J. Alcalá-Fdez, S. García, F.J. Berlanga, A. Fernandez, L. Sánchez, M.J. del Jesus, F. Herrera. KEEL: A Data Mining Software Tool Integrating Genetic Fuzzy Systems. 3rd International Workshop on Genetic and Evolving Fuzzy Systems (GEFS 2008), Witten-Bommerholz (Germany), 83-88, 4-7 March 2008.
  
• [0848] S. García, F. Herrera. Design of Experiments in Computational Intelligence: On the Use of Statistical Inference. Proceedings of the 3rd International Workshop on Hybrid Artificial Intelligent Systems (HAIS08). Lecture Notes in Artificial Intelligence 5271, Springer-Verlag 2008, Burgos (Spain) 4-14, September 2008.
  
• [0847] J.R. Cano, S. García, F. Herrera. Subgroup Discovery in Large Size Data Sets Preprocessed Using Stratified Instance Selection for Increasing the Presence of Minority Classes. Pattern Recognition Letters 29 (2008) 2156-2164. doi: 10.1016/j.patrec.2008.08.001
  
• [0827] 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. doi: 10.1016/j.patcog.2008.02.006
  
• [0853] S. García, F. Herrera. Evolutionary Training Set Selection to Optimize C4.5 in Imbalanced Problems. Proceedings of the 8th International Conference on Hybrid Intelligent Systems (HIS08), Barcelona (Spain), 567-572, September 2008.
  
• [0882] S. García, F. Herrera. An Extension on "Statistical Comparisons of Classifiers over Multiple Data Sets" for all Pairwise Comparisons. Journal of Machine Learning Research 9 (2008) 2677-2694.
  COMPLEMENTARY MATERIAL to the paper: Software and tests description
  
  
• [0892] J. Luengo, S. García, J.R. Cano, F. Herrera. Estudio de la influencia de las medidas de complejidad de los datos en los Sistemas de Clasifcación Basados en Reglas Difusas: Análisis de la Razón Discriminante de Fisher. XIV Congreso Español sobre Tecnologías y Lógica Fuzzy (ESTYLF08) Mieres (Spain), 257-263, 17-19 September 2008.
  
• [0901] S. García. Nuevos Retos en la Selección Evolutiva de Instancias: Escalabilidad, Aprendizaje con Clases no Balanceadas y Caracterización de la Eficacia. Department of Computer Science and Artificial Intelligence (DECSAI), University of Granada. December 2008.
  Advisor: F. Herrera
  

2007 (9)

• [0629] S. García, J.R. Cano, F. Herrera. Un algoritmo memético para la selección de prototipos: Una propuesta eficiente para problemas de tamaño medio. In Proceedings Congreso Español sobre Metaheurísticas, Algoritmos Evolutivos y Bioinspirados (MAEB07), Tenerife (Spain), 563-570, 14-16 February 2007.
  
• [0630] S. García, D. Molina, M. Lozano, F. Herrera. Un estudio experimental sobre el uso de test no paramétricos para analizar el comportamiento de los algoritmos evolutivos en problemas de optimización. In Proceedings Congreso Español sobre Metaheurísticas, Algoritmos Evolutivos y Bioinspirados (MAEB07), Tenerife (Spain), 275-285, 14-16 February 2007.
  
• [0664] A. Fernandez, S. García, M.J. del Jesus, F. Herrera. A study on the Use of the Fuzzy Reasoning Method based on the Winning Rule Vs. Voting Procedure for Classification with Imbalanced Data Sets. Proceedings of the 9th International Work-Conference on Artificial Neural Networks (IWANN07). Lecture Notes on Computer Science 4507, Springer-Verlag, San Sebastián (Spain), 375-382, June 2007.
  
• [0665] A. Fernandez, S. García, M.J. del Jesus, F. Herrera. An Analysis of the Rule Weights and Fuzzy Reasoning Methods for Linguistic Rule Based Classification Systems Applied to Problems with Highly Imbalanced Data Sets. International Workshop on Fuzzy Logic and Applications (WILF07). Lecture Notes in Computer Science 4578, Springer-Verlag 2007, Genova (Italy, 2007), 170-179.
  
• [0719] S. García, D. Molina, F. Herrera, M. Lozano. Tests no paramétricos de comparaciones múltiples con algoritmo de control en el análisis de algoritmos evolutivos: Un caso de estudio con los resultados de la sesión especial en optimización continua. Proceedings of the II Congreso Español de Informática (CEDI 2007). I Jornadas sobre Algoritmos Evolutivos y Metaheurísticas (JAEM'07), Zaragoza (Spain), 219-227, 11-14 September 2007.
  
• [0722] J. Luengo, S. García, F. Herrera. Estudio de la influencia de los métodos de imputación en Redes Neuronales de Base Radial para clasificación. Proceedings of the II Congreso Español de Informática (CEDI 2007). Simposio de Inteligencia Computacional (SICO2007), Zaragoza (Spain), 81-88, 11-14 September 2007.
  
• [0723] S. García, A. Fernandez, A.D. Benítez, F. Herrera. Statistical Comparisons by Means of Non-Parametric Tests: A Case Study on Genetic Based Machine Learning. Proceedings of the II Congreso Español de Informática (CEDI 2007). V Taller Nacional de Minería de Datos y Aprendizaje (TAMIDA 2007), Zaragoza (Spain), 95-104, 11-14 September 2007.
  
• [0724] J.R. Cano, S. García, F. Herrera, E. Bernadó-Mansilla. Analysis of Evolutionary Prototype Selection by means of a Data Complexity Measure based on Class Separability. Proceedings of the II Congreso Español de Informática (CEDI 2007). V Taller Nacional de Minería de Datos y Aprendizaje (TAMIDA 2007), Zaragoza (Spain), 57-63, 11-14 September 2007.
  
• [0725] J. Luengo, S. García, F. Herrera. A Study on the Use of Statistical Tests for Experimentation with Neural Networks. Proceedings of the 9th International Work-Conference on Artificial Neural Networks (IWANN07). Lecture Notes on Computer Science 4507, Springer-Verlag, San Sebastián (Spain), 72-79, June 2007.
  

2006 (4)

• [0611] M.J. del Jesus, A. Fernandez, S. García, F. Herrera. A first study on the use of fuzzy rule based classification systems for problems with imbalanced data sets. Proceedings of the Symposium on Fuzzy Systems in Computer Science (FSCS 2006), Magdeburg (Germany), 63-72, 27-28 September 2006.
  
• [0612] A. Fernandez, S. García, F. Herrera, M.J. del Jesus. Un primer estudio sobre el uso de los sistemas de clasificación basados en reglas difusas en problemas de clasificación con clases no balanceadas. Proceedings of the XIII Congreso Español sobre Tecnologías y Lógica Fuzzy (ESTYLF 2006), Ciudad Real (Spain), 89-95, 20-22 September 2006, ISBN 84-689-9547-9.
  
• [0613] S. García, J.R. Cano, F. Herrera. Incorporating knowledge in evolutionary prototype selection. Proceedings of the 7th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL06). Lecture Notes in Computer Science 4224, Springer-Verlag 2006, Burgos (Spain) 1358-1366, September 2006.
  
• [0614] S. García, J.R. Cano, A. Fernandez, F. Herrera. A proposal of evolutionary prototype selection for class imbalance problems. Proceedings of the 7th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL06). Lecture Notes in Computer Science 4224, Springer-Verlag 2006, Burgos (Spain) 1415-1423, September 2006.