Publications

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Book Chapter
A Review on Evolutionary Prototype Selection, García, S., Cano J. R., and Herrera F. , Intelligent Systems for Automated Learning and Adaptation: Emerging Trends and Applications, p.92–113, (2010)
Conference Paper
A First Attempt on Monotonic Training Set Selection, Cano, J. R., and García S. , Hybrid Artificial Intelligent Systems, Cham, p.277–288, (2018)
A first study on the use of fuzzy rule based classification systems for problems with imbalanced data sets, del Jesus, M. J., Fernández A., García S., and Herrera F. , Proceedings of the Symposium on Fuzzy Systems in Computer Science (FSCS), September, Magdeburg (Germany), p.63-72, (2006)
A Nearest Hyperrectangle Monotonic Learning Method, García, Javier, Cano J. R., and García S. , Proceedings of the 11th International Conference Hybrid Artificial Intelligent Systems, 2016, Seville, Spain, April 18-20, 2016, p.311–322, (2016)
A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines, Charte, David, Charte Francisco, García S., del Jesus M. J., and Herrera F. , XVIII Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA 2018), 10, Granada (Spain), p.949–950, (2018) PDF icon 2018-CAEPIA-TutorialAEs.pdf (59.39 KB)
A Preliminary Study on Selecting the Optimal Cut Points in Discretization by Evolutionary Algorithms, García, S., López V., Luengo J., Carmona C. J., and Herrera F. , 1st International Conference on Pattern Recognition Applications and Methods (ICPRAM), February, Villamoura - (Portugal), p.211-216, (2012) PDF icon 2012 - ICPRAM.pdf (106.28 KB)
A Preliminary Study on the Selection of Generalized Instances for Imbalanced Classification, García, S., Derrac J., Triguero I., Carmona C. J., and Herrera F. , Twenty Third International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems (IEA/AIE), Cordoba, p.601-610, (2010) PDF icon 2010 - IEA-AEI.pdf (211.57 KB)
A proposal of Evolutionary Prototype Selection for Class Imbalance Problems, García, S., Cano J. R., Fernández A., and Herrera F. , Proceedings of the 7th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL), Volume 4224, p.1415-1423, (2006)
A study on the Use of the Fuzzy Reasoning Method based on the Winning Rule Vs. Voting Procedure for Classification with Imbalanced Data Sets, Fernández, A., García S., del Jesus M. J., and Herrera F. , Proceedings of the 9th International Work-Conference on Artificial Neural Networks (IWANN), June, San Sebastián (Spain), p.375-382, (2007)
Addressing Data-Complexity for Imbalanced Data-sets: A Preliminary Study on the Use of Preprocessing for C4.5, Luengo, J., Fernández A., Herrera F., and García S. , 9th International Conference on Intelligent Systems Designs and Applications (ISDA), p.523-528, (2009)
An Analysis of the Rule Weights and Fuzzy Reasoning Methods for Linguistic Rule Based Classification Systems Applied to Problems with Highly Imbalanced Data Sets, Fernández, A., García S., del Jesus M. J., and Herrera F. , International Workshop on Fuzzy Logic and Applications (WILF), July, Genova (Italy), p.170-179, (2007)
Análisis of Evolutionary Prototype Selection by means of a Data Complexity Measure based on Class Separabilty, Cano, J. R., García S., Herrera F., and Bernadó-Mansilla E. , Actas del Taller de Minería de Datos y Aprendizaje (TAMIDA), Zaragoza, p.145-152, (2007)
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, Luengo, J., García S., Cano J. R., and Herrera F. , XIV Congreso Español sobre Tecnologías y Lógica Fuzzy (ESTYLF), September, Mieres (Spain), p.257-263, (2008)
Incorporating Knowledge in Evolutionary Prototype Selection, García, S., Cano J. R., and Herrera F. , Proceedings of the 7th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL), Volume 4224, p.1358-1366, (2006)
KEEL: A Data Mining Software Tool Integrating Genetic Fuzzy Systems, Alcalá-Fdez, J., García S., Berlanga F. J., Fernández A., Sánchez L., del Jesus M. J., and Herrera F. , 3rd International Workshop on Genetic and Evolving Fuzzy Systems (GEFS), WittenBommerholz (Germany), p.83-88, (2008)
Statistical Comparisons by Means of Non-Parametric Tests: A Case Study on Genetic Based Machine Learning, García, S., Fernández A., Benítez A.D., and Herrera F. , Proceedings of the II Congreso Español de Informática (CEDI 2007). V Taller Nacional de Minería de Datos y Aprendizaje (TAMIDA), September, Zaragoza (Spain), p.95-104, (2007)
Un algoritmo memético para la selección de prototipos: Una propuesta eficiente para problemas de tamaño medio, García, S., Cano J. R., and Herrera F. , Proceedings Congreso Español sobre Metaheurísticas, Algoritmos Evolutivos y Bioinspirados (MAEB), Tenerife, (2007)
Un primer estudio sobre el uso de los sistemas de clasificación basados en reglas difusas en problemas de clasificación con clases no balanceadas, Fernández, A., García S., Herrera F., and del Jesus M. J. , Proceedings of the XIII Congreso Español sobre Tecnologías y Lógica Fuzzy (ESTYLF), September, Ciudad Real (Spain), p.89-95, (2006)
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, García, S., Fernández A., and Herrera F. , Proceedings of VI Congreso Español sobre Metaheurísticas, Algoritmos Evolutivos y Bioinspirados (MAEB), February, Málaga (Spain), p.183-190, (2009)
Journal Article
A memetic algorithm for Evolutionary Prototype Selection: A Scaling Up Approach, García, S., Cano J. R., and Herrera F. , Pattern Recognition, Volume 41, Number 8, p.2693-2709, (2008)
A snapshot on nonstandard supervised learning problems: taxonomy, relationships, problem transformations and algorithm adaptations, Charte, David, Charte Francisco, García S., and Herrera F. , Progress in Artificial Intelligence, Nov, (2018)
A snapshot on nonstandard supervised learning problems: taxonomy, relationships, problem transformations and algorithm adaptations, Charte, David, Charte Francisco, García S., and Herrera F. , Progress in Artificial Intelligence, 11, (In Press) PDF icon 2018-PRAI-NonStandard-Accepted.pdf (951.61 KB)
A Study of the Behaviour of Linguistic Fuzzy Rule Based Classification Systems in the Framework of Imbalanced Data Sets, Fernández, A., García S., del Jesus M. J., and Herrera F. , Fuzzy Sets and Systems, Volume 159, Number 18, p.2378-2398, (2008)
Addressing Data Complexity for Imbalanced Data Sets: Analysis of SMOTE-based Oversampling and Evolutionary Undersampling, Luengo, J., Fernández A., García S., and Herrera F. , Soft Computing, Volume 15, Number 10, p.1909-1936, (2011)
Addressing Imbalanced Classification with Instance Generation Techniques: IPADE-ID, López, V., Triguero I., Carmona C. J., García S., and Herrera F. , Neurocomputing, Volume 126, p.15-28, (2014) PDF icon 2014-Lopez-NEUROCOMPUTING.pdf (933.25 KB)
Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental Analysis of Power, García, S., Fernández A., Luengo J., and Herrera F. , Information Sciences, Volume 180, p.2044–2064, (2010)
Diagnose of Effective Evolutionary Prototype Selection using an Overlapping Measure, García, S., Cano J. R., Bernadó-Mansilla E., and Herrera F. , International Journal of Pattern Recognition and Artificial Intelligence, Volume 23, Number 8, p.1527-1548, (2009)
Enhancing the Effectiveness and Interpretability of Decision Tree and Rule Induction Classifiers with Evolutionary Training Set Selection over Imbalanced Problems, García, S., Fernández A., and Herrera F. , Applied Soft Computing, Volume 9, p.1304-1314, (2009)
Evolutionary Selection of Hyperrectangles in Nested Generalized Exemplar Learning, García, S., Derrac J., Luengo J., Carmona C. J., and Herrera F. , Applied Soft Computing, Volume 11, Number 3, p.3032-3045, (2011) PDF icon 2011-Garcia-ASOC.pdf (2.07 MB)
Evolutionary-Based Selection of Generalized Instances for Imbalanced Classification, García, S., Derrac J., Triguero I., Carmona C. J., and Herrera F. , Knowledge-Based Systems, Volume 25, Number 1, p.3-12, (2012) PDF icon 2012-Garcia-KBS.pdf (522.95 KB)
Genetics-Based Machine Learning for Rule Induction: State of the Art, Taxonomy and Comparative Study, Fernández, A., Luengo J., García S., Bernadó-Mansilla E., and Herrera F. , IEEE Transactions on Evolutionary Computation, Volume 14, Number 6, p.913-941, (2010)
Hyperrectangles Selection for Monotonic Classification by Using Evolutionary Algorithms, García, Javier, Al-bar Adnan, Aljohani Naif R., Cano J. R., and García S. , International Journal Computational Intelligence Systems, Volume 9, Number 1, p.184–201, (2016)
KEEL 3.0: An Open Source Software for Multi-Stage Analysis in Data Mining., Triguero, I., Gonzalez S., Moyano J.M., García S., Alcalá-Fdez J., Luengo J., Fernández A., del Jesus M. J., Sánchez L., Herrera F., et al. , International Journal of Computational Intelligence Systems, Volume 10, Number 1, p.1238-1249, (2017)
KEEL: A Software Tool to Assess Evolutionary Algorithms for Data Mining Problems, Alcalá-Fdez, J., Sánchez L., García S., del Jesus M. J., Ventura S., Garrell J.M., Otero J., Romero C., Bacardit J., Rivas V. M., et al. , Soft Computing, Volume 13, Number 3, p.307-318, (2009)
KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework, Alcalá-Fdez, J., Fernández A., Luengo J., Derrac J., García S., Sánchez L., and Herrera F. , Journal of Multiple-Valued Logic and Soft Computing, Volume 17, Number 2-3, p.255-287, (2011)
Label noise filtering techniques to improve monotonic classification, Cano, J. R., Luengo J., and García S. , Neurocomputing, (In Press)
Making CN2-SD Subgroup Discovery Algorithm scalable to Large Size Data Sets using Instance Selection, Cano, J. R., Herrera F., Lozano M., and García S. , Expert Systems with Applications, Volume 35, p.1949-1965, (2008)
MoNGEL: monotonic nested generalized exemplar learning, García, Javier, Fardoun Habib M., Alghazzawi Daniyal M., Cano J. R., and García S. , Pattern Analysis and Applications, May, Volume 20, Number 2, p.441–452, (2017)
MoNGEL: monotonic nested generalized exemplar learning, García, Javier, Fardoun Habib M., Alghazzawi Daniyal M., Cano J. R., and García S. , Pattern Analysis and Applications, p.1–12, (2015)
Prototype Selection for Nearest Neighbor Classification: Taxonomy and Empirical Study, García, S., Derrac J., Cano J. R., and Herrera F. , IEEE Transactions Pattern Analysis and Machiche Intelligence, Volume 34, Number 3, p.417–435, (2012)
Prototype selection to improve monotonic nearest neighbor, Cano, J. R., Aljohani Naif R., Abbasi Rabeeh Ayaz, Alowidbi Jalal S., and García S. , Engineering Applications of Artificial Intelligence, Volume 60, p.128 - 135, (2017)
Subgroup Discovery in Large Size Data Sets Preprocessed Using Stratified Instance Selection for Increasing the Presence of Minority Classes, Cano, J. R., García S., and Herrera F. , Pattern Recognition Letters, Volume 29, p.2156-2164, (2008)
Training set selection for monotonic ordinal classification, Cano, J. R., and García S. , Data & Knowledge Engineering, Volume 112, p.94 - 105, (2017)