Charte Ojeda, F. ., Rivera Rivas, A. J. ., del Jesus Díaz, M. J. ., & Herrera Triguero, F. . (2019). Dealing with difficult minority labels in imbalanced mutilabel data sets. Neurocomputing, 326, 39-53. https://doi.org/10.1016/j.neucom.2016.08.158
Francisco Charte Ojeda
First name
Francisco
Last name
Charte Ojeda
2019
Charte Ojeda, F. ., & García, L. . (2019). El pasado de la computación personal. Historia de la microinformática (2a Edición). Editorial Universidad de Jaén.
Charte Ojeda, F. ., Vico, A. ., Pérez Godoy, M. D. . ., & Rivera Rivas, A. J. . (2019). predtoolsTS: R package for streamlining time series forecasting. Progress in Artificial Intelligence, 8, 505-510. https://doi.org/10.1007/s13748-019-00193-z (Original work published 2019)
Charte Ojeda, F. ., Rivera Rivas, A. J. ., del Jesus Díaz, M. J. ., & Herrera Triguero, F. . (2019). REMEDIAL-HwR: Tackling multilabel imbalance through label decoupling and data resampling hybridization. Neurocomputing, 326, 110-122. https://doi.org/10.1016/j.neucom.2017.01.118
Charte, D. ", Herrera Triguero, F. ., & Charte Ojeda, F. . (2019). Ruta: implementations of neural autoencoders in R. Knowledge-Based Systems, 174, 4-8. https://doi.org/- (Original work published 2019)
Cordon, I. ., Luengo, J. ., García López, S. ., Herrera Triguero, F. ., & Charte Ojeda, F. . (2019). Smartdata: Data preprocessing to achieve smart data in R. Neurocomputing, 360, 1-13. https://doi.org/10.1016/j.neucom.2019.06.006 (Original work published 2019)
Viedma, D. T., Rivera Rivas, A. J. ., Charte Ojeda, F. ., & del Jesus Díaz, M. J. . (2019). A First Approximation to the Effects of Classical Time Series Preprocessing Methods on LSTM Accuracy. 270-280. https://doi.org/10.1007/978-3-030-20521-8_23 (Original work published 2019)
Martínez, F. ., Frías Bustamante, M. del P. ., Charte Ojeda, F. ., & Rivera Rivas, A. J. . (2019). Time Series Forecasting with KNN in R: the tsfknn Package. The R Journal, 11, 229-242. https://doi.org/10.32614/RJ-2019-004 (Original work published 2019)
Charte, D. ", Charte Ojeda, F. ., del Jesus Díaz, M. J. ., & Herrera Triguero, F. . (2019). A Showcase of the Use of Autoencoders in Feature Learning Applications. 412-421. https://doi.org/10.1007/978-3-030-19651-6_40 (Original work published 2019)
Charte, D. ", Charte Ojeda, F. ., García López, S. ., & Herrera Triguero, F. . (2019). A snapshot on nonstandard supervised learning problems: taxonomy, relationships, problem transformations and algorithm adaptations. Progress in Artificial Intelligence, 8, 1-14. https://doi.org/10.1007/s13748-018-00167-7 (Original work published)