Charte Ojeda, F. ., Rivera Rivas, A. J. ., del Jesus Díaz, M. J. ., & Herrera Triguero, F. . (2015). Resampling Multilabel Datasets by Decoupling Highly Imbalanced Labels. 489-501. Bilbao (Spain). https://doi.org/10.1007/978-3-319-19644-2_41 (Original work published)
Maria José del Jesus Díaz
First name
Maria José
Last name
del Jesus Díaz
2015
2014
del Jesus Díaz, M. J. ., Martínez, F. ., Pérez Godoy, M. D. . ., Rivera Rivas, A. J. ., & Frías Bustamante, M. del P. . (2014). An ensemble method for time series forecasting with simple exponential smoothing. Presentado en. Rota, Cádiz (Spain). (Original work published)
Fernández, A. ., del Río, S. ., López, V. ., Bawakid, A. ., del Jesus Díaz, M. J. ., Benitez, J. M., & Herrera Triguero, F. . (2014). Big Data with Cloud Computing: an insight on the computing environment, MapReduce, and programming frameworks. WIREs Data Mining and Knowledge Discovery, 4, 380-409. https://doi.org/10.1002/widm.1134
Charte Ojeda, F. ., Rivera Rivas, A. J. ., del Jesus Díaz, M. J. ., & Herrera Triguero, F. . (2014). Concurrence among Imbalanced Labels and Its Influence on Multilabel Resampling Algorithms. 110-121. Salamanca (Spain). https://doi.org/10.1007/978-3-319-07617-1_10 (Original work published)
Charte Ojeda, F. ., Rivera Rivas, A. J. ., del Jesus Díaz, M. J. ., & Herrera Triguero, F. . (2014). LI-MLC: A Label Inference Methodology for Addressing High Dimensionality in the Label Space for Multilabel Classification. IEEE Transactions on Neural Networks and Learning Systems, 25, 1842-1854. https://doi.org/10.1109/TNNLS.2013.2296501
Charte Ojeda, F. ., Rivera Rivas, A. J. ., del Jesus Díaz, M. J. ., & Herrera Triguero, F. . (2014). MLeNN: A First Approach to Heuristic Multilabel Undersampling. 1-9. Salamanca (Spain). https://doi.org/10.1007/978-3-319-10840-7_1 (Original work published)
Carmona, C. J. ., González García, P. ., del Jesus Díaz, M. J. ., & Herrera Triguero, F. . (2014). Overview on evolutionary subgroup discovery: analysis of the suitability and potential of the search performed by evolutionary algorithms. WIREs Data Mining and Knowledge Discovery, 4, 87-103. https://doi.org/10.1002/widm.1118
Parras Gutiérrez, E. . ., Rivas, V. M., Arenas, M. ., & del Jesus Díaz, M. J. . (2014). Short, medium and long term forecasting of time series using the L-Co-R algorithm. Neurocomputing, 128, 433-446. https://doi.org/10.1016/j.neucom.2013.08.023
Pérez Godoy, M. D. . ., Rivera Rivas, A. J. ., Carmona, C. J. ., & del Jesus Díaz, M. J. . (2014). Training algorithms for Radial Basis Function Networks to tackle learning processes with imbalanced data-sets. Applied Soft Computing, 25, 26-39. https://doi.org/10.1016/j.asoc.2014.09.011
2013
Carmona, C. J. ., González García, P. ., García-Domingo, B. ., del Jesus Díaz, M. J. ., & García, J. J. . A. (2013). An evolutionary fuzzy system for the detection of exceptions in subgroup discovery. 74-79. Edmonton (Canada). (Original work published 2025)