Fernández Hilario, A. L. . ., del Jesus Díaz, M. J. ., & Herrera Triguero, F. . (2010). On the 2-Tuples Based Genetic Tuning Performance for Fuzzy Rule Based Classification Systems in Imbalanced Data-Sets. Information Sciences, 180, 1268-1291. https://doi.org/10.1016/j.ins.2009.12.014
Francisco Herrera Triguero
First name
Francisco
Last name
Herrera Triguero
2010
Alcala-Fdez, J. ., Garcia, S. ., Sánchez, L. ., Robles, I. ., del Jesus Díaz, M. J. ., Bernadó-Mansilla, E. ., … Herrera Triguero, F. . (2010). Introduction to the Experimental Design in the Data Mining Tool KEEL. (Original work published)
García López, S. ., Cano De Amo, J. R. . ., & Herrera Triguero, F. . (2010). A Review on Evolutionary Prototype Selection. https://doi.org/10.4018/978-1-60566-798-0.ch005
Fernández Hilario, A. L. . ., Calderón, M. ., Barrenechea, E. ., Bustince, H. ., & Herrera Triguero, F. . (2010). Solving Multi-Class Problems with Linguistic Fuzzy Rule Based Classification Systems Based on Pairwise Learning and Preference Relations. Fuzzy Sets and Systems, 161, 3064-3080. https://doi.org/10.1016/j.fss.2010.05.016
Fernández Hilario, A. L. . ., del Jesus Díaz, M. J. ., & Herrera Triguero, F. . (2010). Multi-class Imbalanced Data-Sets with Linguistic Fuzzy Rule Based Classification Systems Based on Pairwise Learning. 6178, 89-98. Dortmund (Germany). (Original work published 2025)
García López, S. ., Fernández Hilario, A. L. . ., Luengo, J. ., & Herrera Triguero, F. . (2010). Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental Analysis of Power. Information Sciences, 180, 2044-2064. https://doi.org/10.1016/j.ins.2009.12.010
Fernández Hilario, A. L. . ., del Jesus Díaz, M. J. ., & Herrera Triguero, F. . (2010). Analysing the Hierarchical Fuzzy Rule Based Classification Systems with Genetic Rule Selection. 69-74. Mieres (Spain). (Original work published 2025)
Gacto, M. J., Alcalá, R. ., & Herrera Triguero, F. . (2010). 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. 228-238. Córdoba. (Original work published 2025)
Fernández Hilario, A. L. . ., Luengo, J. ., García López, S. ., Bernadó-Mansilla, E. ., & Herrera Triguero, F. . (2010). Genetics-Based Machine Learning for Rule Induction: State of the Art, Taxonomy and Comparative Study. IEEE Transactions on Evolutionary Computation, 14, 913-941. https://doi.org/10.1109/TEVC.2009.2039140
Berlanga, F. ., Rivera Rivas, A. J. ., del Jesus Díaz, M. J. ., & Herrera Triguero, F. . (2010). GP-COACH: Genetic Programming-based learning of Compact and ACcurate fuzzy rule-based classification systems for High-dimensional problems. Information Sciences, 180, 1183-1200. https://doi.org/10.1016/j.ins.2009.12.020