Francisco Herrera

Charte, D. ", Charte Ojeda, F. ., & Herrera, F. . (2021). Reducing Data Complexity using Autoencoders with Class-informed Loss Functions. IEEE Transactions on Pattern Analysis and Machine Intelligence, In Press. https://doi.org/10.1109/TPAMI.2021.3127698

Charte, D. ", Sevillano-García, I. ., Lucena-González, M. J., Martín-Rodríguez, J. L., Charte Ojeda, F. ., & Herrera, F. . (2021). Slicer: Feature Learning for Class Separability with Least-Squares Support Vector Machine Loss and COVID-19 Chest X-Ray Case Study. En H. S. González, I. P. López, P. G. Bringas, H. . Quintián, & E. . Corchado (Eds.), Hybrid Artificial Intelligent Systems (HAIS 2021) (pp. 305-315). Cham: Springer International Publishing.

Herrera, F. ., Carmona, C. J. ., González, P. ., & del Jesus Díaz, M. J. . (2011). An overview on subgroup discovery: Foundations and applications. Knowledge and Information Systems, 29, 495-525. https://doi.org/10.1007/s10115-010-0356-2 (Original work published)