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)
Francisco Charte Ojeda
First name
Francisco
Last name
Charte Ojeda
2016
Charte Ojeda, F. ., Charte, D. ", Rivera Rivas, A. J. ., del Jesus Díaz, M. J. ., & Herrera Triguero, F. . (2016). R Ultimate Multilabel Dataset Repository. 487-499. Seville (Spain). https://doi.org/10.1007/978-3-319-32034-2_41 (Original work published)
García-Vico, Ángel M. ., Charte Ojeda, F. ., González García, P. ., Carmona, C. J. ., & del Jesus Díaz, M. J. . (2016). Subgroup Discovery with Evolutionary Fuzzy Systems in R: the SDEFSR Package. The R Journal, 8, 307-323.
Charte, D. ", Charte Ojeda, F. ., & Herrera Triguero, F. . (2016). Análisis visual de técnicas no supervisadas de deep learning con el paquete dlvisR. 895-904. Salamanca (Spain). (Original work published)
Martínez, F. ., Pérez Godoy, M. D. . ., Charte Ojeda, F. ., & del Jesus Díaz, M. J. . (2016). Combining simple exponential smoothing models for time series forecasting. 635-644. Granada (Spain). (Original work published)
Charte Ojeda, F. ., Rivera Rivas, A. J. ., Pulgar Rubio, F. J. . ., & del Jesus Díaz, M. J. . (2016). Explotación de la potencia de procesamiento mediante paralelismo: un recorrido histórico hasta la GPGPU. Enseñanza Y Aprendizaje De ingeniería De Computadores. Revista De Experiencias Docentes En ingeniería De Computadores, 6, 19-33.
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
2015
Charte Ojeda, F. ., & Charte, D. ". (2015). Working with Multilabel Datasets in R: The mldr Package. The R Journal, 7, 149-162. https://doi.org/10.32614/RJ-2015-027
Charte Ojeda, F. ., Rivera Rivas, A. J. ., del Jesus Díaz, M. J. ., & Herrera Triguero, F. . (2015). Addressing imbalance in multilabel classification: Measures and random resampling algorithms. Neurocomputing, 163, 3-16. https://doi.org/10.1016/j.neucom.2014.08.091