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Deep learning
Charte Ojeda, F. ., Rivera Rivas, A. J. ., Martínez, F. ., & del Jesus Díaz, M. J. . (2019). Automating Autoencoder Architecture Configuration: An Evolutionary Approach. 339-349. https://doi.org/10.1007/978-3-030-19591-5_35 (Original work published 2019)
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, 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)