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)
Maria José del Jesus Díaz
First name
Maria José
Last name
del Jesus Díaz
2020
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)
2019
García-Vico, Á. M., González García, P., Carmona, C. J., & del Jesus Díaz, M. J. (2019). A Big Data Approach for the Extraction of Fuzzy Emerging Patterns. Cognitive Computation, 11, 400-417. https://doi.org/10.1007/s12559-018-9612-7 (Original work published 2019)
Viedma, D. T., Rivera Rivas, A. J., Charte Ojeda, F., & del Jesus Díaz, M. J. (2019). A First Approximation to the Effects of Classical Time Series Preprocessing Methods on LSTM Accuracy. 270-280. https://doi.org/10.1007/978-3-030-20521-8_23 (Original work published 2019)
Charte, D. ", Charte Ojeda, F., del Jesus Díaz, M. J., & Herrera Triguero, F. (2019). A Showcase of the Use of Autoencoders in Feature Learning Applications. 412-421. https://doi.org/10.1007/978-3-030-19651-6_40 (Original work published 2019)
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)
Charte Ojeda, F., Rivera Rivas, A. J., del Jesus Díaz, M. J., & Herrera Triguero, F. (2019). Dealing with difficult minority labels in imbalanced mutilabel data sets. Neurocomputing, 326, 39-53. https://doi.org/10.1016/j.neucom.2016.08.158
Fernández Hilario, A. L., del Jesus Díaz, M. J., Cordón García, Ó., Marcelloni, F., & Herrera Triguero, F. (2019). Evolutionary Fuzzy Sistems for Explainable Artificial Intelligence: Why, When, What for, and Where to ? IEEE Computational Intelligence, 1, 69-81. https://doi.org/10.1109/TFUZZ.2018.2814577
Charte Ojeda, F., Rivera Rivas, A. J., del Jesus Díaz, M. J., & Herrera Triguero, F. (2019). REMEDIAL-HwR: Tackling multilabel imbalance through label decoupling and data resampling hybridization. Neurocomputing, 326, 110-122. https://doi.org/10.1016/j.neucom.2017.01.118
García-Vico, Á. M., González García, P., Carmona, C. J., & del Jesus Díaz, M. J. (2019). Study on the use of different quality measures within a multi-objective evolutionary algorithm approach for emerging pattern mining in big data environments. Big Data Analytics, 4, 1. https://doi.org/10.1186/s41044-018-0038-8