classification

Sánchez, L. . (2014). Comments on Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization by Eyke Hüllermeier. International Journal of Approximate Reasoning, 55, 1583-1587. https://doi.org/10.1016/j.ijar.2014.04.008
Couso, I. ., & Sánchez, L. . (2016). Machine learning models, epistemic set-valued data and generalized loss functions: An encompassing approach. Information Sciences, 358-359, 129-150. https://doi.org/10.1016/j.ins.2016.04.016
Pulgar Rubio, F. J. . ., Charte Ojeda, F. ., Rivera Rivas, A. J. ., & del Jesus Díaz, M. J. . (2021). ClEnDAE: A classifier based on ensembles with built-in dimensionality reduction through denoising autoencoders. Information Sciences, 565, 146-176. https://doi.org/10.1016/j.ins.2021.02.060
Pulgar Rubio, F. J. . ., Charte Ojeda, F. ., Rivera Rivas, A. J. ., & del Jesus Díaz, M. J. . (2020). Choosing the proper autoencoder for feature fusion based on data complexity and classifiers: Analysis, tips and guidelines. Information Fusion, 54, 44-60. https://doi.org/10.1016/j.inffus.2019.07.004 (Original work published 2020)
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
Cordón García, Óscar ., Quirin, A. ., & Sánchez, L. . (2008). On the Use of Bagging, Mutual Information-Based Feature Selection and Multicriteria Genetic Algorithms to Design Fuzzy Rule-Based Classification Ensembles. 549-554. https://doi.org/10.1109/HIS.2008.147 (Original work published 2025)
García, J. J. . A., Chica, M. ., del Jesus Díaz, M. J. ., & Herrera Triguero, F. . (2007). Niching genetic feature selection algorithms applied to the design of fuzzy rule-based classification systems. 1-6. https://doi.org/10.1109/FUZZY.2007.4295638 (Original work published 2025)