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)
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)
De la teoría a la práctica: una reflexión sobre el EEES en aula, Romero, Samuel, Cano J. R., Prados-Suarez María Belen, and Rivero-Cejudo Maria Linarejos , Adaptación del profesorado universitario al espacio europeo de educación superior mediante el uso de nuevas tecnologías, Number 69-77, Jaén (España), (2005)
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)
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)
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)
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 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 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 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)
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)
Credal C4.5 with Refinement of Parameters, Mantas, Carlos J., Abellán Joaquín, Castellano Javier G., Cano J. R., and Moral Serafín , Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications, Cham, p.739–747, (2018)
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)
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)
Scrae Web: Sistema de Corrección y Revisión Automática de Exámenes a Través de la Web, Pulido, Alfredo Sanchez, Cano J. R., and Pavón-Pulido Nieves , Jornadas de Enseñanza Universitaria de la Informática Jenui , 01, Cáceres (España), (2002)
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)
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)
Journal Article
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 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)
Analysis of data complexity measures for classification, Cano, J. R. , Expert Systems with Applications, Volume 40, Number 12, p.4820–4831, (2013)
CommuniMents: A Framework for Detecting Community Based Sentiments for Events, Jarwar, Muhammad Aslam, Abbasi Rabeeh Ayaz, Mushtaq Mubashar, Maqbool Onaiza, Aljohani Naif R., Daud Ali, Alowibdi Jalal S., Cano J. R., García Salvador, and Chong Ilyoung , International Journal on Semantic Web and Information Systems, Volume 13, Issue 2, p.87-108, (2017)
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)
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)
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)
Label noise filtering techniques to improve monotonic classification, Cano, J. R., Luengo J., and García S. , Neurocomputing, (In Press)
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)
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)
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)
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)
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)
Predictive-collaborative model as recovery and validation tool. Case of study: Psychiatric emergency department decision support, Cano, J. R. , Expert Systems with Applications, Volume 39, Number 4, p.4044–4048, (2012)
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)
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)
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)
Training set selection for monotonic ordinal classification, Cano, J. R., and García S. , Data & Knowledge Engineering, Volume 112, p.94 - 105, (2017)
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)