Francisco Charte Ojeda

García-Vico, Ángel M. ., Elizondo, D. ., Charte Ojeda, F. ., González García, P. ., & Carmona, C. J. . (2021). E2PAMEA: un algoritmo evolutivo para la extraccióni eficiente de patrones emergentes difusos en entornos big data. Conferencia De La Asociación Española Para La Inteligencia Artificial (CAEPIA). Presented at the.

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. In 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.

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)

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)