M.Górriz, J. ., Ramírez, J. ., Ortíz, A. ., Martínez-Murcia, F. J., Segovia, F. ., Suckling, J. ., … Ferrández, J. M. (2020). Artificial intelligence within the interplay between natural and artificial computation: Advances in data science, trends and applications. Neurocomputing, 410, 237-270. https://doi.org/10.1016/j.neucom.2020.05.078
Francisco Herrera Triguero
First name
Francisco
Last name
Herrera Triguero
2020
Charte, D. ", Charte Ojeda, F. ., del Jesus Díaz, M. J. ., & Herrera Triguero, F. . (2020). An analysis on the use of autoencoders for representation learning: Fundamentals, learning task case studies, explainability and challenges. Neurocomputing, 404, 93-107. https://doi.org/10.1016/j.neucom.2020.04.057
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)
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
Fernández Hilario, A. L. . ., del Jesus Díaz, M. J. ., Cordón García, Óscar ., Marcelloni, F. ., & Herrera Triguero, F. . (2019). Evolutionary Fuzzy Sistems for Explainable Artificial Intelligence: Why, When, What for, and Where to ?. IEEE Computational Intelligence, 1, 69-81. https://doi.org/10.1109/TFUZZ.2018.2814577
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)
2018
Carmona, C. J. ., del Jesus Díaz, M. J. ., & Herrera Triguero, F. . (2018). A Unifying Analysis for the Supervised Descriptive Rule Discovery via the Weighted Relative Accuracy. Knowledge-Based Systems, 139, 89-100. https://doi.org/10.1016/j.knosys.2017.10.015