Fernández Hilario, A. L. . ., Berlanga, F. ., del Jesus Díaz, M. J. ., & Herrera Triguero, F. . (2009). Genetic Cooperative-Competitive Fuzzy Rule Based Learning Method using Genetic Programming for Highly Imbalanced Data-Sets. 42-47. Lisbon (Portugal).
Francisco Herrera Triguero
First name
Francisco
Last name
Herrera Triguero
2009
Gacto, M. J., Alcalá, R. ., & Herrera Triguero, F. . (2009). Handling High-Dimensional Regression Problems by Means of an Efficient Multi-Objective Evolutionary Algorithm. 109-114. Pisa (Italy). (Original work published 2025)
Gacto, M. J., Alcalá, R. ., & Herrera Triguero, F. . (2009). A Multiobjective Evolutionary Algorithm for Tuning Fuzzy Rule Based Systems with Measures for Preserving Interpretability. 1146-1151. Lisbon, Portugal: IFSA/EUSFLAT 2009. (Original work published 2025)
Fernández Hilario, A. L. . ., del Jesus Díaz, M. J. ., & Herrera Triguero, F. . (2009). Hierarchical fuzzy rule based classfication systems with genetic rule selection for imbalanced data-sets. International Journal of Approximate Reasoning, 50, 561-577. https://doi.org/10.1016/j.ijar.2008.11.004
Pérez Godoy, M. D. . ., Rivera Rivas, A. J. ., Fernández Hilario, A. L. . ., del Jesus Díaz, M. J. ., & Herrera Triguero, F. . (2009). A Preliminar Analysis of CO2RBFN in Imbalanced Problems (J. . Cabestany, F. . Sandoval, A. . Prieto, & J. M. Corchado, Eds.). Berlin, Heidelberg: Springer Berlin Heidelberg.
Alcalá, R. ., Alcala-Fdez, J. ., Gacto, M. J., & Herrera Triguero, F. . (2009). Improving Fuzzy Logic Controllers Obtained by Experts: A Case Study in HVAC Systems. Applied Intelligence, 31, 15-30. https://doi.org/10.1007/s10489-007-0107-6
Gacto, M. J., Alcalá, R. ., & Herrera Triguero, F. . (2009). Adaptation and Application of Multi-Objective Evolutionary Algorithms for Rule Reduction and Parameter Tuning of Fuzzy Rule-Based Systems. Soft Computing, 13, 419-436. https://doi.org/10.1007/s00500-008-0359-z
Fernández Hilario, A. L. . ., del Jesus Díaz, M. J. ., & Herrera Triguero, F. . (2009). Improving the Performance of Fuzzy Rule Based Classification Systems for Highly Imbalanced Data-sets Using an Evolutionary Adaptive Inference System. 5517, 294-301. Salamanca (Spain). (Original work published 2025)
Luengo, J. ., Fernández Hilario, A. L. . ., Herrera Triguero, F. ., & García López, S. . (2009). Addressing Data-Complexity for Imbalanced Data-sets: A Preliminary Study on the Use of Preprocessing for C4.5. 523-528.
Alcala-Fdez, J. ., Sánchez, L. ., García López, S. ., del Jesus Díaz, M. J. ., Ventura, S. ., Garrell, J. ., … Herrera Triguero, F. . (2009). KEEL: A Software Tool to Assess Evolutionary Algorithms for Data Mining Problems. Soft Computing, 13, 307-318. https://doi.org/10.1007/s00500-008-0323-y