Publications

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Book
Multilabel Classification: Problem Analysis, Metrics and Techniques, Herrera, F., Charte Francisco, Rivera-Rivas A.J., and del Jesus M. J. , (2016)
Book Chapter
- A Multiobjective Genetic Algorithm for Feature Selection and Data Base Learning in Fuzzy-Rule Based Classification Systems, Cordón, O., Herrera F., del Jesus M. J., Magdalena L., Sánchez A.M., and Villar P. , Intelligent Systems for Information Processing, Amsterdam, p.315 - 326, (2003)
A Multiobjective Genetic Learning Process for joint Feature Selection and Granularity and Contexts Learning in Fuzzy Rule-Based Classification Systems, Cordón, O., del Jesus M. J., Herrera F., Magdalena Luis, and Villar Pedro , Interpretability Issues in Fuzzy Modeling, Berlin, Heidelberg, p.79–99, (2003)
A Multiobjective Genetic Learning Process for joint Feature Selection and Granularity and Contexts Learning in Fuzzy Rule-Based Classification Systems, Cordón, O., del Jesus M. J., Herrera F., Magdalena Luis, and Villar Pedro , Interpretability Issues in Fuzzy Modeling, Berlin, Heidelberg, p.79–99, (2003)
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)
A Study on the Combination of Evolutionary Algorithms and Stratified Strategies for Training Set Selection in Data Mining, Cano, J. R., Herrera F., and Lozano M. , Soft Computing: Methodologies and Applications, p.271-284, (2005)
Different Proposals to Improve the Accuracy of Fuzzy Linguistic Modeling, Cordón, O., Herrera F., del Jesus M. J., Villar Pedro, and Zwir Igor , Fuzzy If-Then Rules in Computational Intelligence: Theory and Applications, Boston, MA, p.189–221, (2000)
Evolutionary Induction of Descriptive Rules in a Market Problem, del Jesus, M. J., González P., Herrera F., and Mesonero M. , Intelligent Data Mining. Techniques and Applications, Studies in Computational Intelligence, Volume 5, p.267-292, (2005)
Extacción de conocimiento con algoritmos evolutivos y reglas difusas, Díaz, María José del, González P., and Herrera F. , Introducción a la minería de datos, p.383-420, (2004)
Fuzzy Rule Reduction and Tuning of Fuzzy Logic Controllers for a HVAC System, Alcalá, R., Alcalá-Fdez J., Gacto M. J., and Herrera F. , Fuzzy Applications in Industrial Engineering, Studies in Fuzziness and Soft Computing, Volume 201, p.89-117, (2006)
Instance Selection Using Evolutionary Algorithms: An Experimental Study, Cano, J. R., Herrera F., and Lozano Manuel , Advanced Techniques in Knowledge Discovery and Data Mining, London, p.127–152, (2005)
Introduction to the Experimental Design in the Data Mining Tool KEEL, Alcala-Fdez, Jesus, Garcia S, Sánchez Luciano, Robles I, del Jesus M. J., Bernado-Mansilla E, Peregrin Antonio, and Herrera F. , 01, p.1-25, (2010)
On the use of Multiobjective Genetic Algorithms to Improve the Accuracy-Interpretability Trade-Off of Fuzzy Rule-Based Systems, Alcalá, R., Alcalá-Fdez J., Gacto M. J., and Herrera F. , Multi-objective Evolutionary Algorithms for Knowledge Discovery from Data Bases, Volume 98, (2008)
On the Usefulness of MOEAs for Getting Compact FRBSs Under Parameter Tuning and Rule Selection, Alcalá, R., Alcalá-Fdez J., Gacto M. J., and Herrera F. , Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases, Berlin, Heidelberg, p.91–107, (2008)
Proyecto KEEL: Desarrollo de una herramienta para el análisis e implementación de algoritmos de extracción de conocimiento evolutivos, Alcala-Fdez, Jesus, del Jesus M. J., Garrell Josep-Maria, Herrera F., Martínez Cesar, and Sánchez Luciano , 01, p.413-424, (2004)
Replacement Strategies to Maintain Useful Diversity in Steady-State Genetic Algorithms, Lozano, Manuel, Herrera F., and Cano J. R. , 01, p.85-96, (2005)
Selección Evolutiva Estratificada de Conjuntos de Entrenamiento para la Obtención de Bases de Reglas con un Alto Equilibrio entre Precisión e Interpretabilidad, Cano, J. R., Herrera F., and Lozano M. , Tendencias de la Minería de Datos en Spain., p.263 - 274, (2004)
Strategies for Scaling Up Evolutionary Instance Reduction Algorithms for Data Mining, Cano, J. R., Herrera F., and Lozano Manuel , Evolutionary Computation in Data Mining, Berlin, Heidelberg, p.21–39, (2005)
Subgroup Discovery with Linguistic Rules, del Jesus, M. J., González P., and Herrera F. , Fuzzy Sets and Their Extensions: Representation, Aggregation and Models, Berlin, Heidelberg, p.411–430, (2008)
Subgroup Discovery with Linguistic Rules, del Jesus, M. J., González P., and Herrera F. , Fuzzy Sets and Their Extensions: Representation, Aggregation and Models, Berlin, Heidelberg, p.411–430, (2008)
Técnicas de reducción de datos en KDD, Cano, J. R., and Herrera F. , Minería de datos: Técnicas y Aplicaciones, Number 13-33, Sevilla (España), (2005)
Tuning fuzzy partitions or assigning weights to fuzzy rules: which is better?, Sánchez, Luciano, and Otero José , Accuracy Improvements in Linguistic Fuzzy Modeling, Berlin, Heidelberg, p.366–385, (2003)
Un estudio empírico preliminar sobre los tests estadísticos más habituales en el aprendizaje automático, Herrera, F., Hervas-Martínez Cesar, Otero J, and Sánchez Luciano , Tendencias de la minería de datos en España, Number 403-412, Santander (España), (2004)
Conference Paper
A double axis classi, Gacto, M. J., Alcalá R., and Herrera F. , 9th International Workshop on Fuzzy Logic and Applications (WILF), (2011)
A Double Axis Classification of Interpretability Measures for Linguistic Fuzzy Rule-Based Systems, Gacto, M. J., Alcalá R., and Herrera F. , Fuzzy Logic and Applications, Berlin, Heidelberg, p.99–106, (2011)
A First Approach to Deal with Imbalance in Multi-label Datasets, Charte, Francisco, Rivera-Rivas A.J., del Jesus M. J., and Herrera F. , 8th International Conference on Hybrid Artificial Intelligent Systems (HAIS 2013), 9, Salamanca (Spain), p.150-160, (2013) PDF icon 2013-HAIS-ImbalanceMultilabel.pdf (194.4 KB)
A first approach towards a fuzzy decision tree for multilabel classification, Prati, Ronaldo C., Charte Francisco, and Herrera F. , 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 7, Naples (Italy), p.1–6, (2017) PDF icon 2017-IEEE-FuzzyDTMLC.pdf (299.08 KB)
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 Genetic-Programming-Based Approach for the Learning of Compact Fuzzy Rule-Based Classification Systems, Berlanga, F. J., del Jesus M. J., Gacto M. J., and Herrera F. , The Eighth International Conference on Artificial Intelligence and Soft Computing (ICAISC), Volume 4029, Zakopane (Poland), p.182-191, (2006)
A GRASP Algorithm for Clustering, Cano, J. R., Cordón O., Herrera F., and Sánchez Luciano , Proceedings of the 8th Ibero-American Conference on Artifical Intelligence, Seville, Spain, November 12-15, 2002,, p.214–223, (2002)
A Multiobjective Evolutionary Algorithm for Tuning Fuzzy Rule Based Systems with Measures for Preserving Interpretability, Gacto, M. J., Alcalá R., and Herrera F. , Proceedings of the Joint International Fuzzy Systems Association World Congress and the European Society for Fuzzy Logic and Technology Conference (IFSA), July, Lisbon, Portugal, p.1146-1151, (2009)
A multiobjective genetic algorithm for feature selection and granularity learning in fuzzy-rule based classification systems, Cordón, O., Herrera F., del Jesus M. J., and Villar P , 08, Volume 3, p.1253 - 1258 vol.3, (2001)
A Novel Genetic Cooperative-Competitive Fuzzy Rule Based Learning Method using Genetic Programming for High Dimensional Problems, Berlanga, F. J., del Jesus M. J., and Herrera F. , 3rd International Workshop on Genetic and Evolving Fuzzy Systems (GEFS), WittenBommerholz (Germany), p.101-106, (2008)
A Pareto Based Ensemble with Feature and Instance Selection for Learning from Multi-Class Imbalanced Datasets, Fernández, A., Carmona C. J., del Jesus M. J., and Herrera F. , Proc. of the XVIII Conferencia de la Asociación Española para la Inteligencia Artificial (XVIII CAEPIA), p.1316-1317, (2018) PDF icon 2018 - CAEPIA-262.pdf (63.54 KB)
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 Preliminar Analysis of CO2RBFN in Imbalanced Problems, Pérez-Godoy, M.D., Rivera-Rivas A.J., Fernández A., del Jesus M. J., and Herrera F. , Bio-Inspired Systems: Computational and Ambient Intelligence, Berlin, Heidelberg, p.57–64, (2009)
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 Showcase of the Use of Autoencoders in Feature Learning Applications, Charte, David, Charte Francisco, del Jesus M. J., and Herrera F. , International Work-Conference on the Interplay Between Natural and Artificial Computation, 05/2019, p.412-421, (2019)
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)
Addressing Overlapping in Classification with Imbalanced Datasets: A First Multi-objective Approach for Feature and Instance Selection, Fernández, Alberto, del Jesus M. J., and Herrera F. , Intelligent Data Engineering and Automated Learning – IDEAL 2015, Cham, p.36–44, (2015)
Ajuste Evolutivo Lateral y de Amplitud de etiquetas para Sistemas Basados en Reglas Difusas, Alcalá, R., Alcalá-Fdez J., Gacto M. J., and Herrera F. , Simposio de Inteligencia Computacional (SICO), Granada (Spain), p.481-488, (2005)
Algoritmo Evolutivo de Extracción de reglas de Asociación aplicado a un problema de marketing, del Jesus, M. J., González P., Herrera F., and Mesonero M. , III Congreso Español de Metaheurísticas, Algoritmos Evolutivos y Bioinspirados(MAEB), Córdoba (Spain), p.102-104, (2004)
Algoritmo Genético Multi-Objetivo Avanzado para el ajuste de un sistema difuso aplicado al Control de Sistemas de Ventilación, Calefacción y Aire Acondicionado, Gacto, M. J., Alcalá R., and Herrera F. , Proceedings of the Congreso Español sobre Metaheurísticas, Algoritmos Evolutivos y Bioinspirados (MAEB), February, p.595-602, (2009)
An analysis of evolutionary algorithms with different types of fuzzy rules in subgroup discovery, Carmona, C. J., González P., del Jesus M. J., and Herrera F. , IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), August, ICC Jeju, Jeju Island, Korea, p.1706-1711, (2009) PDF icon 2009 - FUZZIEEE.pdf (192.81 KB)
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)
An evolutionary paradigm for designing fuzzy rule-based systems from examples, Cordón, O., del Jesus M. J., Herrera F., and Lozano Manuel , 10, p.139 - 144, (1997)
An Improved Multi-Objective Genetic Algorithm for Tuning Linguistic Fuzzy System, Gacto, M. J., Alcalá R., and Herrera F. , Proceedings of the 2008 International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU), June, p.1121-1128, (2008)
An Study on the Combination of Evolutionary Algorithms and Stratified Strategies for Training Set Selection in Data Mining, Cano, J. R., Herrera F., and Lozano M. , Proceedings of the 8th Online World Conference on Soft Computing in Industrial Applications, September, (2003)
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)
Análisis visual de técnicas no supervisadas de deep learning con el paquete dlvisR, Charte, David, Charte Francisco, and Herrera F. , XVII Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA 2016), 9, Salamanca (Spain), p.895–904, (2016) PDF icon 2016-CAEPIA-dlvisR.pdf (2.56 MB)
Analysing the Hierarchical Fuzzy Rule Based Classification Systems with Genetic Rule Selection, Fernández, A., del Jesus M. J., and Herrera F. , 4th International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS), March, Mieres (Spain), p.69-74, (2010)
Analysis of the Impact of Using Different Diversity Functions for the Subgroup Discovery Algorithm NMEEF-SD, Carmona, C. J., González P., del Jesus M. J., and Herrera F. , 5th International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS), April, Paris (France), p.17-23, (2011) PDF icon 2011 - GEFS.pdf (6.67 MB)
Analysis of the Performance of a Semantic Interpretability-Based Tuning and Rule Selection of Fuzzy Rule-Based Systems by Means of a Multi-Objective Evolutionary Algorithm, Gacto, M. J., Alcalá R., and Herrera F. , LNAI 6097, June, Córdoba, p.228-238, (2010)
Aplicación de Algoritmos Evolutivos de Descubrimiento de Subgrupos en e-Learning: un Caso de Estudio, Romero, C., González P., Ventura S., del Jesus M. J., and Herrera F. , V Congreso Español sobre Metaheurísticas, Algoritmos Evolutivos y Bioinspirados (MAEB), Tenerife (Spain), p.493-500, (2007)
Aprendizaje de reglas difusas mediante programación genética en problemas con alta dimensionalidad, Berlanga, F. J., del Jesus M. J., and Herrera F. , I Simposio sobre Lógica Fuzzy y Soft Computing (LFSC), Granada (Spain), p.93-100, (2005)
Aprendizaje Evolutivo de los Contextos de las Funciones de Pertenencia para Extraer Reglas de Asociación Difusas, Alcalá-Fdez, J., Alcalá R., Gacto M. J., and Herrera F. , Proceedings of the II Congreso Español de Informática (CEDI 2007). II Simposio sobre Lógica Fuzzy y Soft Computing (LFSC), September, p.25-32, (2007)
Aprendizaje Evolutivo De Sistemas Aproximativos De Tipo TSK Para Problemas De Alta Dimensionalidad, Gacto, M. J., Alcalá R., and Herrera F. , XVI Congreso Español sobre Tecnologías y Lógica Fuzzy (ESTYLF), February, Valladolid (Spain), p.295-300, (2012)
Atipicidad: Medida de calidad clave dentro del descubrimiento de reglas descriptivas supervisadas, Carmona, C. J., del Jesus M. J., and Herrera F. , Proc. of the XVIII Conferencia de la Asociación Española para la Inteligencia Artificial (XVIII CAEPIA), p.827-828, (2018) PDF icon 2018 - CAEPIA-70.pdf (67.19 KB)
Computing the spanish medium electrical line maintenance costs by means of evolution-based learning processes, Cordón, O., Herrera F., and Sánchez Luciano , Methodology and Tools in Knowledge-Based Systems, Berlin, Heidelberg, p.478–486, (1998)
Concurrence among Imbalanced Labels and Its Influence on Multilabel Resampling Algorithms, Charte, Francisco, Rivera-Rivas A.J., del Jesus M. J., and Herrera F. , 9th International Conference on Hybrid Artificial Intelligent Systems (HAIS 2014), 6, Salamanca (Spain), p.110–121, (2014) PDF icon 2014-HAIS-ConcurrenceLabels.pdf (1.12 MB)
Cost Sensitive and Preprocessing for Classification with Imbalanced Data-sets: Similar Behaviour and Potential Hybridizations, López, Victoria, Fernández Alberto, del Jesus M. J., and Herrera F. , ICPRAM 2012 - Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods, 02, Volume 2, (2012)
Estimación de la Longitud de Línea de Baja Tensión Mediante Técnicas Evolutivas de Análisis de Datos, Cordón, O., Spín Antonio, Fajardo Waldo, Herrera F., and Sánchez Luciano , 8ª Reunión Nacional de Grupos de Investigación en Ingeniería Eléctrica, (1998)
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)
Evolutionary approaches to the learning of fuzzy rule-based classification systems, Cordón, O., Herrera F., and del Jesus M. J. , (1999)
Evolutionary Multi-Objective Algorithm to Effectively Improve the Performance of the Classic Tuning of Fuzzy Logic Controllers for a Heating, Ventilating and Air Conditioning System, Gacto, M. J., Alcalá R., and Herrera F. , 5th International Workshop On Genetic And Evolutionary Fuzzy Systems (GEFS), April, (2011)
Extracción de reglas DNF difusas en un problema de marketing, del Jesus, M. J., González P., Herrera F., and Mesonero M. , XII Congreso Español de Tecnologías y Lógica Difusa(ESTYLF), Jaén (Spain), p.351-356, (2004)
Extracción Evolutiva de Reglas de Asociación en un Servicio de Urgencias Psiquiátricas, Aguilera, J., del Jesus M. J., González P., Herrera F., Navío M., and Sáinz J. , II Congreso español sobre Metaheurísticas, Algoritmos evolutivos y bioinspirados(MAEB), Gijón(Spain), p.548-555, (2003)
Feature Selection Algorithms Applied to Parkinson's Disease, Navío, M., Aguilera José, del Jesus M. J., González R., Herrera F., and Iríbar C. , Medical Data Analysis, Berlin, Heidelberg, p.195–200, (2001)
Genetic Cooperative-Competitive Fuzzy Rule Based Learning Method using Genetic Programming for Highly Imbalanced Data-Sets, Fernández, A., Berlanga F. J., del Jesus M. J., and Herrera F. , 13 th International Fuzzy Systems Association World Congress and 6th European Society for Fuzzy Logic and Tecnology Conference (IFSA-EUSFLAT), Lisbon (Portugal), p.42-47, (2009)
Genetic Lateral and Amplitude Tuning of Membership Functions for Fuzzy Systems, Alcalá, R., Alcalá-Fdez J., Gacto M. J., and Herrera F. , Proceedings of the 2nd International Conference on Machine Intelligence (ACIDCA-ICMI), p.589-595, (2005)
Genetic Lateral and Amplitude Tuning with Rule Selection for Fuzzy Control of Heating, Ventilating and Air Conditioning Systems, Alcalá, R., Alcalá-Fdez J., Berlanga F. J., Gacto M. J., and Herrera F. , 19th International Conference on Industrial & Engineering Applications of Artificial Intelligence & Expert Systems (IEA/AIE), Volume 4031, Annecy (France), p.452-461, (2006)
Genetic Learning of Membership Functions for Mining Fuzzy Association Rules, Alcalá, R., Alcalá-Fdez J., Gacto M. J., and Herrera F. , Proceedings of the 16th IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), July, London (United Kingdom), p.1538-1543, (2007)
Genetic tuning of fuzzy rule-based systems integrating linguistic hedges, Casillas, J., Cordón O., Herrera F., and del Jesus M. J. , Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569), July, Volume 3, p.1570-1574 vol.3, (2001)
Handling High-Dimensional Regression Problems by Means of an Efficient Multi-Objective Evolutionary Algorithm, Gacto, M. J., Alcalá R., and Herrera F. , 9th International Conference on Intelligent Systems Design and Applications (ISDA), November, Pisa (Italy), p.109-114, (2009)
Hibridación de métodos filtro y de envoltura para selección de características, García, Jose Joaquin Ag, Díaz María José del, and Herrera F. , Conferencia de la asociación Española para la inteligencia artificial. III Jornada de transferencia tecnológica de inteligencia artificial., 01, Murcia (España), (1999)
Improving Fuzzy Rule-Based Decision Models by Means of a Genetic 2-Tuples Based Tuning and the Rule Selection, Alcalá, R., Alcalá-Fdez J., Berlanga F. J., Gacto M. J., and Herrera F. , Modeling Decisions for Artificial Intelligence (MDAI), Volume 3885, Tarragona (Spain), p.317-328, (2006)
Improving Multi-label Classifiers via Label Reduction with Association Rules, Charte, Francisco, Rivera-Rivas A.J., del Jesus M. J., and Herrera F. , 7th International Conference on Hybrid Artificial Intelligent Systems (HAIS 2012), 9, Salamanca (Spain), p.188–199, (2012) PDF icon 2012-HAIS-LabelReduction.pdf (171.47 KB)
Improving the Performance of Fuzzy Rule Based Classification Systems for Highly Imbalanced Data-sets Using an Evolutionary Adaptive Inference System, Fernández, A., del Jesus M. J., and Herrera F. , 10th International Work-Conference on Artificial Neural Networks (IWANN), June, Volume 5517, Salamanca (Spain), p.294-301,, (2009)
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)
Indice de Interpretabilidad Semántica para el Ajuste de Sistemas Basados en Reglas Difusas y Selección de Reglas Mediante un Algoritmo Evolutivo Multi-Objetivo, Gacto, M. J., Alcalá R., and Herrera F. , XV Edición del Congreso Español sobre Tecnologías y Lógica Fuzzy (ESTYLF), February, Huelva, p.73-78, (2010)
Inducción evolutiva multiobjetivo de reglas de descripción de subgrupos en un problema de marketing, del Jesus, M. J., González P., and Herrera F. , IV Congreso Español sobre Metaheurísticas, Algoritmos Evolutivos y Bioinspirados (MAEB), Granada (Spain), p.661-669, (2005)
Influencia de la granularidad y de las medidas de calidad en SDIGA, González, P., Carmona C. J., del Jesus M. J., and Herrera F. , XIV Congreso Español de Tecnologías y Lógica Difusa( ESTYLF), September, Langreo-Mieres (Spain), (2008) PDF icon 2008b - ESTYLF.pdf (338.36 KB)
Influencia del uso de modificadores lingüísticos y grados de certeza en un sistema de clasificación basados en reglas difusas, del Jesus, M. J., and Herrera F. , X Congreso Español sobre tecnologías y reglas difusas, 09, Santander (España), (2000)
Intelligent Systems in Long-Term Forecasting of the Extra-Virgin Olive Oil Price in the Spanish Market, Pérez-Godoy, M.D., Pérez Pedro, Rivera-Rivas A.J., del Jesus M. J., Frías María Pilar, and Parras Manuel , Trends in Applied Intelligent Systems, Berlin, Heidelberg, p.205–214, (2010)
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)
Learning compact fuzzy rule-based classification systems with genetic programming, Berlanga, F. J., del Jesus M. J., and Herrera F. , 4th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT), Barcelona (Spain), p.1027-1032, (2005)
Learning fuzzy rules using genetic programming: Context-free grammar definition for high-dimensionality problems, Berlanga, F. J., del Jesus M. J., and Herrera F. , I International Workshop on Genetic Fuzzy Systems (GFS), Granada (Spain), p.136-141, (2005)
Métodos de razonamiento aproximado basados en el concepto de mayoría difusa para sistemas de clasificación, Cordón, O., Díaz María José del, and Herrera F. , Congreso español sobre tecnología y lógica fuzzy, 09, Pamplona (España), (1998)
MLeNN: A First Approach to Heuristic Multilabel Undersampling, Charte, Francisco, Rivera-Rivas A.J., del Jesus M. J., and Herrera F. , 15th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2014, 9, Salamanca (Spain), p.1-9, (2014) PDF icon 2014-IDEAL-MLeNN.pdf (184.04 KB)
MLSMOTE: Approaching imbalanced multilabel learning through synthetic instance generation, Charte, Francisco, Rivera-Rivas A.J., del Jesus M. J., and Herrera F. , XVII Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA 2016), 9, Salamanca (Spain), p.821–822, (2016) PDF icon 2016-CAEPIA-MLSMOTE.pdf (579.04 KB)
Modelos Evolutivos de Extracción de Conocimiento en Aplicaciones Médicas: Enfermedad de Parkinson y Urgencias PSiquiátricas, Aguilera, José, Díaz María José del, González P., Herrera F., Navío M., and Saiz J. , Workshop de Minería de Datos y Aprendizaje Automático, 01, Santander (España), (2002)
Multi-class Imbalanced Data-Sets with Linguistic Fuzzy Rule Based Classification Systems Based on Pairwise Learning, Fernández, A., del Jesus M. J., and Herrera F. , 13th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU), June, Volume 6178, Dortmund (Germany), p.89-98, (2010)
Multiobjective Evolutionary Induction of Subgroup Discovery Fuzzy Rules: A Case Study in Marketing, Berlanga, F. J., del Jesus M. J., González P., Herrera F., and Mesonero M. , 6th Industrial Conference on Data Mining (ICDM), Volume 4065, Leipzig (Germany), p.337-349, (2006)
Multiobjective evolutionary induction of subgroup discovery rules in a market problem, Berlanga, F. J., del Jesus M. J., González P., and Herrera F. , 2nd International Conference on Machine Intelligence (ACIDCA-ICMI), Tozeur (Tunisia), p.610-617, (2005)
Multiobjective genetic algorithm for extractiong subgroup discovery fuzzy rules, González, P., del Jesus M. J., and Herrera F. , 2007 IEEE Symposium on Computational Intelligence in Multicriteria Decision Making (IEEE MCDM), Honolulu (USA), p.50-57, (2007)
Multi-Objective Genetic Fuzzy Systems: On the Necessity of Including Expert Knowledge in the MOEA Design Process, Gacto, M. J., Alcalá R., and Herrera F. , Proceedings of the 2008 International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU), June, p.1446-1453, (2008)
Niching genetic feature selection algorithms applied to the design of fuzzy rule-based classification systems, Aguilera, José, Chica M., del Jesus M. J., and Herrera F. , 2007 IEEE International Fuzzy Systems Conference, July, p.1-6, (2007)
Non-dominated Multi-objective Evolutionary Algorithm Based on Fuzzy Rules Extraction for Subgroup Discovery, Carmona, C. J., González P., del Jesus M. J., and Herrera F. , Proceedings of the Fourth International Conference on Hybrid Artificial Intelligence Systems (HAIS), June, Volume 5572, Salamanca (Spain), p.573-580, (2009) PDF icon 2009 - HAIS.pdf (166.16 KB)
Nuevos métodos de razonamiento en sistemas de clasificación basados en reglas difusas, Cordón, O., Díaz María José del, and Herrera F. , Congreso español sobre tecnologías y lógica fuzzy., Tarragona, (1997)
Obtaining accurate TSK Fuzzy Rule-Based Systems by Multi-Objective Evolutionary Learning in high-dimensional regression problems, Gacto, M. J., Galende M., Alcalá R., and Herrera F. , IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 07/2013, p.1-7, (2013) PDF icon 2013-MJGacto.pdf (364.78 KB)
Obtaining Compact and Still Accurate Linguistic Fuzzy Rule-Based Systems by Using Multi-Objetive Genetic Algorithms, Alcalá, R., Alcalá-Fdez J., Gacto M. J., and Herrera F. , Proceedings of the Symposium on Fuzzy Systems in Computer Science (FSCS), p.53-62, (2006)
On the Impact of Dataset Complexity and Sampling Strategy in Multilabel Classifiers Performance, Charte, Francisco, Rivera-Rivas A.J., del Jesus M. J., and Herrera F. , 11th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2016, 4, Seville (Spain), p.500–511, (2016) PDF icon Complexity.pdf (1.56 MB)
{QUINTA: A question tagging assistant to improve the answering ratio in electronic forums}, Charte, Francisco, Rivera-Rivas A.J., del Jesus M. J., and Herrera F. , IEEE International Conference on Computer as a Tool, EUROCON 2015, 9, Salamanca (Spain), p.1-6, (2015) PDF icon 2015-EUROCON-QUINTA.pdf (2.03 MB)
R Ultimate Multilabel Dataset Repository, Charte, Francisco, Charte David, Rivera-Rivas A.J., del Jesus M. J., and Herrera F. , 11th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2016, 4, Seville (Spain), p.487–499, (2016) PDF icon RUMDR.pdf (294.87 KB)
Replacement Strategies to Maintain Useful Diversity in Steady-State Genetic Algorithms, Lozano, M., Herrera F., and Cano J. R. , Proceedings of the 8th Online World Conference on Soft Computing in Industrial Applications, September, (2003)
Resampling Multilabel Datasets by Decoupling Highly Imbalanced Labels, Charte, Francisco, Rivera-Rivas A.J., del Jesus M. J., and Herrera F. , 10th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2015, 6, Bilbao (Spain), p.489–501, (2015) PDF icon 2015-HAIS-REMEDIAL.pdf (1.04 MB)
Selección Evolutiva de Instancias en Minería de Datos, Cano, J. R., Herrera F., and Lozano Manuel , Workwhop de Minería de Datos y Aprendizaje Automático, 01, Santander (España), (2002)
Selecting Fuzzy-Ruled Based Classification System with Specific Reasoning Methods Using Genetical Algorithms, Cordón, O., Díaz María José del, and Herrera F. , Joint 9th IFSA World congres and 20th Nafips International, 07, Praga, (2001)
Sistema de Clasificación con Reglas Difusas Utilizando Algoritmos Genéticos, Cordón, O., Díaz María José del, and Herrera F. , VI Congreso Español sobre Tecnologías y Lógica Fuzzy (ESTYLF'96), Oviedo, (1996)
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 estudio sobre el uso de algoritmos genéticos multimodales para selección de características, Aguilera, José, J J., Chica Manuel, del Jesus M. J., J M., Herrera F., and , 02, (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 el uso de los sistemas de clasificación basados en reglas difusas en problemas de clasificación con clases no balanceadas, Fernández, Alberto, García Salvador, Herrera F., and del Jesus M. J. , XIV Congreso Español sobre tecnologías y lógica fuzzy, 01, Ciudad Real (Español), (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)
Un sistema de clasificación basado en reglas difusas jerárquico con programación genética para problemas de clasificación altamente no balanceados, López, Victoria, Fernández Alberto, del Jesus M. J., and Herrera F. , 02, (2012)
Utilización de Algoritmos Genéticos Multiobjetivos para la Selección de Características y Diseño de la Base de Conocimiento de un Sistema de Clasificación Basado en Reglas Difusas, Cordón, O., Herrera F., Díaz María José del, Magdalena Luis, Sánchez A.M., and Villar Pedro , Congreso Español sobre Tecnologías y Lógica Fuzzy, 01, León (España), (2002)
Journal Article
A Fast and Scalable Multi-Objective Genetic Fuzzy System for Linguistic Fuzzy Modeling in High-Dimensional Regression Problems, Alcalá, R., Gacto M. J., and Herrera F. , IEEE Transactions on Fuzzy Systems, Volume 19, Number 4, p.666-681, (2011)
A Genetic Tuning to Improve the Performance of Fuzzy Rule-Based Classification Systems with Interval-Valued Fuzzy Sets: Degree of Ignorance and Lateral Position, Sanz, J., Fernández A., Bustince H., and Herrera F. , International Journal of Approximate Reasoning, Volume 52, Number 6, p.751-766, (2011)
A greedy randomized adaptive search procedure applied to the clustering problem as an initialization process using K-Means as a local search procedure, Cano, J. R., Cordón O., Herrera F., and Sánchez Luciano , Journal of Intelligent and Fuzzy Systems, Volume 12, Number 3-4, p.235–242, (2002)
A hierarchical genetic fuzzy system based on genetic programming for addressing classification with highly imbalanced and borderline data-sets, López, Victoria, Fernandez Alberto, del Jesus M. J., and Herrera F. , Knowledge-Based Systems, Volume 38, p.85 - 104, (2013)
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 Multi-Objective Evolutionary Algorithm for an Effective Tuning of Fuzzy Logic Controllers in Heating, Ventilating and Air Conditioning Systems, Gacto, M. J., Alcalá R., and Herrera F. , Applied Intelligence, Volume 36, Number 2, p.330-347, (2012)
A Multi-objectiveGenetic Algorithm for Tuning and Rule Selection to Obtain Accurate and Compact Linguistic Fuzzy Rule-Based Systems, Alcalá, R., Gacto M. J., Herrera F., and Alcalá-Fdez J. , International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, Volume 15, Number 5, p.539–557, (2007)
A Pareto Based Ensemble with Feature and Instance Selection for Learning from Multi-Class Imbalanced Datasets, Fernández, A., Carmona C. J., del Jesus M. J., and Herrera F. , International Journal of Neural Systems, Volume 27, Issue 6, p.1-17, (2017) PDF icon 2017-Fernandez-IJNS.pdf (683.56 KB)
A proposal on Reasoning Methods in Fuzzy Rule-Based Classification Systems, Cordón, O., del Jesus M. J., and Herrera F. , International Journal of Approximate Reasoning, Volume 20, p.21-45, (1999)
A Review on Ensembles for Class Imbalance Problem: Bagging, Boosting and Hybrid Based Approaches, Galar, M., Fernández A., Barrenechea E., Bustince H., and Herrera F. , IEEE Transactions on System, Man and Cybernetics - Part C: Applications and Reviews, Volume 42, Number 4, p.463-484, (2012)
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, Volume 8, p.1-14, (2019) PDF icon 2018-PRAI-NonStandard-Accepted.pdf (951.61 KB)
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 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)
A Unifying Analysis for the Supervised Descriptive Rule Discovery via the Weighted Relative Accuracy, Carmona, C. J., del Jesus M. J., and Herrera F. , Knowledge-Based Systems, Volume 139, p.89-100, (2018) PDF icon 2018-Carmona-KBS.pdf (1.57 MB)
A View on Fuzzy Systems for Big Data: Progress and Opportunities, Fernández, A., Carmona C. J., del Jesus M. J., and Herrera F. , International Journal of Computational Intelligence Systems, Volume 9, Number 1, p.69-80, (2016) PDF icon 2016-Fernandez-IJCIS.pdf (455.58 KB)
Adaptation and Application of Multi-Objective Evolutionary Algorithms for Rule Reduction and Parameter Tuning of Fuzzy Rule-Based Systems, Gacto, M. J., Alcalá R., and Herrera F. , Soft Computing, Volume 13, Number 5, p.419-436, (2009)
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 imbalance in multilabel classification: Measures and random resampling algorithms, Charte, Francisco, Rivera-Rivas A.J., del Jesus M. J., and Herrera F. , Neurocomputing, Volume 163, p.3–16, (2015) PDF icon 2015-Neucom-AddressingImbalance.pdf (546.81 KB)
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)
An analysis on the use of autoencoders for representation learning: Fundamentals, learning task case studies, explainability and challenges, Charte, David, Charte Francisco, del Jesus M. J., and Herrera F. , Neurocomputing, Volume 404, p.93-107, (2020) PDF icon 1-s2.0-S092523122030624X-main.pdf (0 bytes)
An overview on Subgroup Discovery: Foundations and Applications, Herrera, F., Carmona C. J., González P., and del Jesus M. J. , Knowledge and Information Systems, Volume 29, Number 3, p.495-525, (2011) PDF icon 2011-Herrera-KAIS.pdf (553.73 KB)
Analysing the classification of imbalanced data-sets with multiple classes: Binarization techniques and ad-hoc approaches, Fernandez, Alberto, López Victoria, Galar Mikel, del Jesus M. J., Herrera F., and BV ELSEVIER S. C. I. E. N. C. E. , Knowledge-Based Systems, Volume 42, p.97 - 110, (2013)
Analysis of preprocessing vs. cost-sensitive learning for imbalanced classification. Open problems on intrinsic data characteristics, López, V., Fernández A., Moreno-Torres J.G., and Herrera F. , Expert Systems with Applications, Volume 39, Number 7, p.6585-6608, (2012)
Analyzing the Reasoning Mechanism in Fuzzy Rule-Based Classification Systems, Cordón, O., del Jesus M. J., and Herrera F. , Mathware & Soft Computing, Volume 5, p.321-332, (1998)
Analyzing the Reasoning Mechanisms in Fuzzy Rule-Based Classification Systems, Cordón, O., del Jesus M. J., and Herrera F. , Mathware & Soft Computing, Volume 5, p.321-332, (1999)
Artificial intelligence within the interplay between natural and artificial computation: Advances in data science, trends and applications, M.Górriz, Juan, Ramírez Javier, Ortíz Andrés, Martínez-Murcia Francisco J., Segovia Fermín, Suckling John, Leming Matthew, Zhang Yu-Dong, Álvarez-Sánchez José Ramón, Bologna Guido, et al. , Neurocomputing, Volume 410, p.237-270, (2020)
Big Data with Cloud Computing: an insight on the computing environment, MapReduce, and programming frameworks, Fernández, Alberto, del Río Sara, López Victoria, Bawakid Abdullah, del Jesus M. J., Benitez Jose M., and Herrera F. , WIREs Data Mining and Knowledge Discovery, Volume 4, Number 5, p.380-409, (2014)
Dealing with difficult minority labels in imbalanced mutilabel data sets, Charte, Francisco, Rivera-Rivas A.J., del Jesus M. J., and Herrera F. , Neurocomputing, Volume 326, p.39–53, (2019) PDF icon 2019-NeucomDealingDifficultLabels.pdf (4.77 MB)
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 algorithms for subgroup discovery in e-learning: A practical application using Moodle data, Romero, C., González P., Ventura S., del Jesus M. J., and Herrera F. , Expert Systems with Applications, Volume 36, p.1632-1644, (2009)
Evolutionary fuzzy rule induction process for subgroup discovery: a case study in marketing, del Jesus, M. J., González P., Herrera F., and Mesonero M. , IEEE Transactions on Fuzzy Systems, Volume 15, Number 4, p.578-592, (2007)
Evolutionary Fuzzy Sistems for Explainable Artificial Intelligence: Why, When, What for, and Where to ?, Fernández, A., del Jesus M. J., Cordón O., Marcelloni F., and Herrera F. , IEEE Computational Intelligence, Volume 1, Number 14, p.69-81, (2019) PDF icon 08610271.pdf (1.09 MB)
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 Stratified Instance Selection applied to Training Set Selection for Extracting High Precise-Interpretable Classification Rules, Cano, J. R., Herrera F., and Lozano Manuel , 01, (2008)
Evolutionary Stratified Training Set Selection for Extracting Classification Rules with trade off Precision-Interpretability, Cano, J. R., Herrera F., and Lozano M. , Data & Knowledge Engineering, Volume 60, Number 1, p.90-108, (2007)
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)
Feature Selection and Granularity Learning in Genetic Fuzzy Rule-Based Classication Systems for Highly Imbalanced Data-Sets, Villar, P., Fernández A., Carrasco R., and Herrera F. , International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, Volume 20, Number 3, p.369-397, (2012)
Genetic Feature Selection in a Fuzzy Rule-Based Classification System Learning Process, Casillas, J., Cordón O., del Jesus M. J., and Herrera F. , Information Sciences, Volume 136, p.135-157, (2001)
Genetic feature selection in a fuzzy rule-based classification system learning process for high-dimensional problems, Casillas, J, Cordón O., del Jesus M. J., and Herrera F. , Information Sciences, Volume 136, Number 1, p.135 - 157, (2001)
Genetic Learning of Fuzzy Rule-Based Classification Systems Cooperating with Fuzzy Reasoning Methods, Cordón, O., del Jesus M. J., and Herrera F. , International Journal of Intelligent Systems, Volume 13, Number 10/11, p.1025-1053, (1998)
Genetic tuning of fuzzy rule deep structures preserving interpretability for linguistic modeling, Casillas, J., Cordón O., del Jesus M. J., and Herrera F. , IEEE Transactions on Fuzzy Systems, Volume 13, Number 1, p.13-29, (2005)
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)
GP-COACH: Genetic Programming-based learning of Compact and ACcurate fuzzy rule-based classification systems for High-dimensional problems, Berlanga, F.J., Rivera-Rivas A.J., del Jesus M. J., and Herrera F. , Information Sciences, Volume 180, Number 8, p.1183 - 1200, (2010)
Hierarchical fuzzy rule based classfication systems with genetic rule selection for imbalanced data-sets, Fernández, A., del Jesus M. J., and Herrera F. , International Journal of Approximate Reasoning, Volume 50, p.561-577, (2009)
Improving Fuzzy Logic Controllers Obtained by Experts: A Case Study in HVAC Systems, Alcalá, R., Alcalá-Fdez J., Gacto M. J., and Herrera F. , Applied Intelligence, Volume 31, Number 1, p.15-30, (2009)
Improving the Performance of Fuzzy Rule-Based Classification Systems with Interval-Valued Fuzzy Sets and Genetic Amplitude Tuning, Sanz, J., Fernández A., Bustince H., and Herrera F. , Information Sciences, Volume 180, Number 19, p.3674-3685, (2010)
Integration of an Index to Preserve the Semantic Interpretability in the Multi-Objective Evolutionary Rule Selection and Tuning of Linguistic Fuzzy Systems, Gacto, M. J., Alcalá R., and Herrera F. , IEEE Transactions on Fuzzy Systems, Volume 18, Number 3, p.515-531, (2010)
Interpretability of Linguistic Fuzzy Rule-Based Systems: An Overview of Interpretability Measures, Gacto, M. J., Alcalá R., and Herrera F. , Information Sciences, Volume 181, Number 20, p.4340–4360, (2011)
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)
Learning the Membership Function Contexts for Mining Fuzzy Association Rules by Using Genetic Algorithms, Alcalá-Fdez, J., Alcalá R., Gacto M. J., and Herrera F. , Fuzzy Sets and Systems, Volume 160, Number 7, p.905-921, (2009)
LI-MLC: A Label Inference Methodology for Addressing High Dimensionality in the Label Space for Multilabel Classification, Charte, Francisco, Rivera-Rivas A.J., del Jesus M. J., and Herrera F. , IEEE Transactions on Neural Networks and Learning Systems, Volume 25, Number 10, p.1842-1854, (2014) PDF icon 2014-TNNLS-LI-MLC.pdf (1.85 MB)
Linguistic Modeling with Hierarchical Systems of Weighted Linguistic Rules, Alcalá, R., Cano J. R., Cordón O., Herrera F., Villar P., and Zwir I. , International Journal of Approximate Reasoning, Volume 32, Number 2-3, p.187-215, (2003)
Making CN1 -SD Subgroup Discovery Algorithm Scalable to Large Size Data Sets Using Instance Selection, Cano, J. R., Herrera F., Lozano Manuel, and García Salvador , Expert System with Applications, Volume 35, Number 4, p.1949-1965, (2008)
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)
METSK-HDe: A Multiobjective Evolutionary Algorithm to learn accurate TSK-fuzzy Systems in High-Dimensional and Large-Scale Regression Problems, Gacto, M. J., Galende M., Alcalá R., and Herrera F. , Information Sciences, Volume 276, p.63–79, (2014) PDF icon 2014-MJGacto-METSK-HDe.pdf (792.59 KB)
MLSMOTE: Approaching imbalanced multilabel learning through synthetic instance generation, Charte, Francisco, Rivera-Rivas A.J., del Jesus M. J., and Herrera F. , Knowledge-Based Systems, Volume 89, p.385–397, (2015) PDF icon 2015-KBS-MLSMOTE.pdf (1.56 MB)
Modelado cualitativo utilizando una metodología evolutiva de aprendizaje iterativo de bases de reglas difusas, Cordón, O., del Jesus M. J., Herrera F., and Lozano M. , Revista Iberoamericana de Inteligencia Artificial, Volume 50, p.56-61, (1998)
Modelo predictivo colaborativo de apoyo al diagnóstico en servicio de urgencias psiquiátricas, Cano, J. R., González P., Aguilera José, López-Herrera A.G., Herrera F., Navío M., and Angel Jiménez-Arriero Miguel , Revista Ibérica de Sistemas y Tecnologías de la información, Volume 4, Number 4, p.29-42, (2009)
MOGUL: A Methodology to Obtain Genetic fuzzy rule-based systems Ander the iterative rule Learning Approach, Cordón, O., del Jesus M. J., and Herrera F. , International Journal of Intelligent Systems, Volume 14, p.1123-1153, (1999)
NMEEF-SD: Non-dominated Multi-objective Evolutionary algorithm for Extracting Fuzzy rules in Subgroup Discovery, Carmona, C. J., González P., del Jesus M. J., and Herrera F. , IEEE Transactions on Fuzzy Systems, Volume 18, Number 5, p.958-970, (2010) PDF icon 2010-Carmona-TFS.pdf (457.11 KB)
On the 2-Tuples Based Genetic Tuning Performance for Fuzzy Rule Based Classification Systems in Imbalanced Data-Sets, Fernández, A., del Jesus M. J., and Herrera F. , Information Sciences, Volume 180, Number 8, p.1268-1291, (2010)
On the Combination of Evolutionary Algorithms and Stratified Strategies for Training Set Selection in Data Mining, Cano, J. R., Herrera F., and Lozano M. , Applied Soft Computing, Volume 6, p.323-332, (2006)
On the influence of an adaptive inference system in fuzzy rule-based classification sytems for imbalanced data-sets, Fernández, A., Herrera F., and del Jesus M. J. , Expert Systems with Applications, Volume 36, Number 6, p.9805-9812, (2009)
Overview on evolutionary subgroup discovery: analysis of the suitability and potential of the search performed by evolutionary algorithms, Carmona, C. J., González P., del Jesus M. J., and Herrera F. , WIREs Data Mining and Knowledge Discovery, Volume 4, Number 2, p.87-103, (2014) PDF icon 2014-Carmona-WIRE-DMKD.pdf (958.42 KB)
Preface: Special Issue on Genetic Fuzzy Systems and the Interpretability–Accuracy Trade-off, Casillas, Jorge, Herrera F., Pérez Raúl, and Villar P , International Journal of Approximate Reasoning, 01, Volume 44, p.1-3, (2007)
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)
REMEDIAL-HwR: Tackling multilabel imbalance through label decoupling and data resampling hybridization, Charte, Francisco, Rivera-Rivas A.J., del Jesus M. J., and Herrera F. , Neurocomputing, Volume 326, p.110–122, (2019) PDF icon 2019-NeucomRemedial-HwR.pdf (2.83 MB)
Replacement Strategies to Preserve Useful Diversity in Steady-State Genetic Algorithms, Lozano, M., Herrera F., and Cano J. R. , Information Sciences, (2012)
Replacement strategies to preserve useful diversity in steady-state genetic algorithms, Lozano, M., Cano J. R., and Herrera F. , Information Sciences, Volume 178, Number 23, p.4421–4433, (2008)
Revisiting Evolutionary Fuzzy Systems: Taxonomy, applications, new trends and challenges, Fernandez, Alberto, López Victoria, del Jesus M. J., and Herrera F. , Knowledge-Based Systems, Volume 80, p.109 - 121, (2015)
Rule Base Reduction and Genetic Tuning of Fuzzy Systems based on the Linguistic 3-Tuples Representation, Alcalá, R., Alcalá-Fdez J., Gacto M. J., and Herrera F. , Soft Computing, Volume 11, Number 5, p.401-419, (2007)
Ruta: implementations of neural autoencoders in R, Charte, David, Herrera F., and Charte Francisco , Knowledge-Based Systems, 06/2019, Volume 174, p.4-8, (2019) PDF icon 1-s2.0-S0950705119300140-main.pdf (421.73 KB)
Smartdata: Data preprocessing to achieve smart data in R, Cordon, I., Luengo Julián, García Salvador, Herrera F., and Charte Francisco , Neurocomputing, 09/2019, Volume 360, p.1-13, (2019)
Solving Electrical Distribution Problems Using Hybrid Evolutionary Data Analysis Techniques, Cordón, O., Herrera F., and Sánchez Luciano , Applied Intelligence, Jan, Volume 10, Number 1, p.5–24, (1999)
Solving Multi-Class Problems with Linguistic Fuzzy Rule Based Classification Systems Based on Pairwise Learning and Preference Relations, Fernández, A., Calderón M., Barrenechea E., Bustince H., and Herrera F. , Fuzzy Sets and Systems, Volume 161, Number 23, p.3064-3080, (2010)
Special issue on genetic fuzzy systems and the interpretability-accuracy trade-off, Casillas, J., Herrera F., Pérez F.G.R., del Jesus M. J., and Villar P. , International Journal of Approximate Reasoning, Volume 44, p.1-3, (2007)
Stratification for Scaling Up Evolutionary Prototype Selection, Cano, J. R., Herrera F., and Lozano M. , Pattern Recognition Letters, Volume 26, p.953-963, (2005)
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)
Tips, guidelines and tools for managing multi-label datasets: The mldr.datasets R package and the Cometa data repository, Charte, Francisco, Rivera-Rivas A.J., Charte David, del Jesus M. J., and Herrera F. , Neurocomputing, Volume 289, p.68–85, (2018) PDF icon 2018-Neucom-TipsMLCCometa-compressed.pdf (1017.86 KB)
Using Evolutionary Algorithms as Instance Selection for Data Reduction in KDD: an Experimental Study, Cano, J. R., Herrera F., and Lozano M. , IEEE Transactions on Evolutionary Computation, Volume 7, Number 6, p.561-575, (2003)