García-Vico, Á. M., Carmona, C. J., & del Jesus Díaz, M. J. (2017). Análisis de Diferentes Tipos de Reglas en Sistemas Difusos Evolutivos para Minería de Patrones Emergentes. 876-885.
Maria José del Jesus Díaz
First name
Maria José
Last name
del Jesus Díaz
2017
García-Vico, Á. 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.
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, Á. 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, Á. 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