Francisco Charte Ojeda

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.

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

Charte Ojeda, F. . (2020). A Comprehensive and Didactic Review on Multilabel Learning Software Tools. IEEE Access, 8, 50330-50354. https://doi.org/10.1109/ACCESS.2020.2979787 (Original work published 2020)

Charte, D. ", Charte Ojeda, F. ., del Jesus Díaz, M. J. ., & Herrera Triguero, F. . (2020). An analysis on the use of autoencoders for representation learning: Fundamentals, learning task case studies, explainability and challenges. Neurocomputing, 404, 93-107. https://doi.org/10.1016/j.neucom.2020.04.057

M.Górriz, J. ., Ramírez, J. ., Ortíz, A. ., Martínez-Murcia, F. J., Segovia, F. ., Suckling, J. ., … Ferrández, J. M. (2020). Artificial intelligence within the interplay between natural and artificial computation: Advances in data science, trends and applications. Neurocomputing, 410, 237-270. https://doi.org/10.1016/j.neucom.2020.05.078

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)

García-Vico, Ángel M. ., Charte Ojeda, F. ., González García, P. ., Elizondo, D. ., & Carmona, C. J. . (2020). E2PAMEA: A fast evolutionary algorithm for extracting fuzzy emerging patterns in big data environments. Neurocomputing, 415, 60-73. https://doi.org/10.1016/j.neucom.2020.07.007 (Original work published 2020)

Charte Ojeda, F. ., Rivera Rivas, A. J. ., Medina, J. ., & Espinilla, M. . (2020). El ecosistema de aprendizaje del estudiante universitario en la post-pandemia. Metodologías y herramientas. Enseñanza Y Aprendizaje De Ingeniería De Computadores. https://doi.org/10.30827/Digibug.64779

Charte Ojeda, F. ., Rivera Rivas, A. J. ., Martínez, F. ., & del Jesus Díaz, M. J. . (2020). EvoAAA: An evolutionary methodology for automated neural autoencoder architecture search. Integrated Computer-Aided Engineering, 27, 211-231. https://doi.org/10.3233/ICA-200619 (Original work published 2020)