Puentes-Marchal, F. ., Pérez-Godoy, M. ., González, P. ., & del Jesus Díaz, M. J. . (2021). Implementation of Data Stream Classification Neural Network Models Over Big Data Platforms. https://doi.org/10.1007/978-3-030-85099-9_22 (Original work published)
Maria José del Jesus Díaz
First name
Maria José
Last name
del Jesus Díaz
2021
García-Vico, Ángel M. ., Carmona, C. J. ., González García, P. ., & del Jesus Díaz, M. J. . (2021). A cellular-based evolutionary approach for the extraction of emerging patterns in massive data streams. Expert Systems With Applications, 183, 115419.
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
García-Vico, Ángel M. ., Seker, H. ., Carmona, C. J. ., González García, P. ., & del Jesus Díaz, M. J. . (2021). FEPDS: Una propuesta para la extracción de patrones emergentes difusos en flujos continuos de datos. Presented at the.
2020
Carmona, C. J. ., González García, P. ., García-Vico, Ángel M. ., & del Jesus Díaz, M. J. . (2020). A Preliminary Many Objective Approach for Extracting Fuzzy Emerging Patterns. 1268, 100. https://doi.org/10.1007/978-3-030-57802-2_10
Rivera Rivas, A. J. ., Pérez Godoy, M. D. . ., Elizondo, D. ., Deka, L. ., & del Jesus Díaz, M. J. . (2020). A Preliminary Study on Crop Classification with Unsupervised Algorithms for Time Series on Images with Olive Trees and Cereal Crops. 276-285. https://doi.org/10.1007/978-3-030-57802-2_27 (Original work published 2020)
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)
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
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)
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)