Alcala-Fdez, J., García López, S., Berlanga, F., Fernández Hilario, A. L., Sánchez, L., del Jesus Díaz, M. J., & Herrera Triguero, F. (2008). KEEL: A Data Mining Software Tool Integrating Genetic Fuzzy Systems. 83-88. WittenBommerholz (Germany).
Francisco Herrera Triguero
First name
Francisco
Last name
Herrera Triguero
2008
Berlanga, F., del Jesus Díaz, M. J., & Herrera Triguero, F. (2008). A Novel Genetic Cooperative-Competitive Fuzzy Rule Based Learning Method using Genetic Programming for High Dimensional Problems. 101-106. WittenBommerholz (Germany).
Cano De Amo, J. R., Herrera Triguero, F., Lozano, M., & García López, S. (2008). Making CN1 -SD Subgroup Discovery Algorithm Scalable to Large Size Data Sets Using Instance Selection. Expert System with Applications, 35, 1949-1965.
Fernández Hilario, A. L., García López, S., del Jesus Díaz, M. J., & Herrera Triguero, F. (2008). A Study of the Behaviour of Linguistic Fuzzy Rule Based Classification Systems in the Framework of Imbalanced Data Sets. Fuzzy Sets and Systems, 159, 2378-2398. https://doi.org/10.1016/j.fss.2007.12.023
Cano De Amo, J. R., Herrera Triguero, F., Lozano Márquez, M., & García López, S. (2008). Making CN2-SD Subgroup Discovery Algorithm scalable to Large Size Data Sets using Instance Selection. Expert Systems with Applications, 35, 1949-1965.
Gacto, M. J., Alcalá, R., & Herrera Triguero, F. (2008). An Improved Multi-Objective Genetic Algorithm for Tuning Linguistic Fuzzy System. 1121-1128. (Original work published 2026)
Gacto, M. J., Alcalá, R., & Herrera Triguero, F. (2008). Multi-Objective Genetic Fuzzy Systems: On the Necessity of Including Expert Knowledge in the MOEA Design Process. 1446-1453. (Original work published 2026)
Alcalá, R., Alcala-Fdez, J., Gacto, M. J., & Herrera Triguero, F. (2008). On the use of Multiobjective Genetic Algorithms to Improve the Accuracy-Interpretability Trade-Off of Fuzzy Rule-Based Systems (A. Ghosh, S. Dehuri, & S. Ghosh, Eds.).
Alcalá, R., Alcala-Fdez, J., Gacto, M. J., & Herrera Triguero, F. (2008). On the Usefulness of MOEAs for Getting Compact FRBSs Under Parameter Tuning and Rule Selection (A. Ghosh, S. Dehuri, & S. Ghosh, Eds.). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-77467-9_5
Lozano Márquez, M., Cano De Amo, J. R., & Herrera Triguero, F. (2008). Replacement strategies to preserve useful diversity in steady-state genetic algorithms. Information Sciences, 178, 4421-4433. https://doi.org/10.1016/j.ins.2008.07.031