Puentes, F., Pérez Godoy, M. D., González García, P., & del Jesus Díaz, M. J. (2020). An analysis of technological frameworks for data streams. Progress in Artificial Intelligence, 9, 239-261. https://doi.org/10.1007/s13748-020-00210-6 (Original work published 2020)
Maria José del Jesus Díaz
First name
Maria José
Last name
del Jesus Díaz
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
2019
Luna, J. M., Carmona, C. J., García-Vico, Á. M., del Jesus Díaz, M. J., & Ventura, S. (2019). Subgroup Discovery on Multiple Instance Data. International Journal of Computational Intelligence Systems, 12, 1602-1612. https://doi.org/10.2991/ijcis.d.191213.001 (Original work published 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