García-Vico, Ángel M. ., González García, P. ., Carmona, C. J. ., & del Jesus Díaz, M. J. . (2017). Impact of the Type of Rule in Fuzzy Emerging Pattern Mining on a Big Data Approach. Presentado en.
Maria José del Jesus Díaz
First name
Maria José
Last name
del Jesus Díaz
2017
Triguero, I. ., Gonzalez, S. ., Moyano, J. ., García López, S. ., Alcala-Fdez, J. ., Luengo, J. ., … PRESS., A. . (2017). KEEL 3.0: An Open Source Software for Multi-Stage Analysis in Data Mining. International Journal of Computational Intelligence Systems, 10, 1238-1249.
Pulgar Rubio, F. J. . ., Rivera Rivas, A. J. ., Pérez Godoy, M. D. . ., González García, P. ., Carmona, C. J. ., & del Jesus Díaz, M. J. . (2017). MEFASD-BD: Multi-Objective Evolutionary Algorithm for Subgroup Discovery in Big Data Environments - A MapReduce Solution. Knowledge-Based Systems, 117, 70-78. https://doi.org/10.1016/j.knosys.2016.08.021
Pulgar Rubio, F. J. . ., Rivera Rivas, A. J. ., Charte Ojeda, F. ., & del Jesus Díaz, M. J. . (2017). On the Impact of Imbalanced Data in Convolutional Neural Networks Performance. 220-232. La Rioja (Spain). https://doi.org/10.1007/978-3-319-59650-1_19 (Original work published)
García-Vico, Ángel M. ., González García, P. ., del Jesus Díaz, M. J. ., & Carmona, C. J. . (2017). A First Approach to Handle Emergining Patterns Mining on Big Data Problems: The EvAEFP-Spark Algorithm. 1-6.
Fernández Hilario, A. L. . ., Carmona, C. J. ., del Jesus Díaz, M. J. ., & Herrera Triguero, F. . (2017). A Pareto Based Ensemble with Feature and Instance Selection for Learning from Multi-Class Imbalanced Datasets. International Journal of Neural Systems, 27, 1-17. https://doi.org/10.1142/S0129065717500289
2016
García-Vico, Ángel M. ., Carmona, C. J. ., González García, P. ., & del Jesus Díaz, M. J. . (2016). Minería de Patrones Emergentes: Una oportunidad para la extracción evolutiva de conocimiento. Presentado en.
Charte Ojeda, F. ., Rivera Rivas, A. J. ., del Jesus Díaz, M. J. ., & Herrera Triguero, F. . (2016). MLSMOTE: Approaching imbalanced multilabel learning through synthetic instance generation. XVII Conferencia De La Asociación Española Para La Inteligencia Artificial (CAEPIA 2016), 821-822. Salamanca (Spain). (Original work published)
Herrera Triguero, F. ., Charte Ojeda, F. ., Rivera Rivas, A. J. ., & del Jesus Díaz, M. J. . (2016). Multilabel Classification: Problem Analysis, Metrics and Techniques. Springer. https://doi.org/10.1007/978-3-319-41111-8
Charte Ojeda, F. ., Rivera Rivas, A. J. ., del Jesus Díaz, M. J. ., & Herrera Triguero, F. . (2016). On the Impact of Dataset Complexity and Sampling Strategy in Multilabel Classifiers Performance. 500-511. Seville (Spain). https://doi.org/10.1007/978-3-319-32034-2_42 (Original work published)