David Charte

Charte, D. ", Charte Ojeda, F. ., & Herrera, F. . (2021). Reducing Data Complexity using Autoencoders with Class-informed Loss Functions. IEEE Transactions on Pattern Analysis and Machine Intelligence, In Press. https://doi.org/10.1109/TPAMI.2021.3127698

Charte, D. ", Sevillano-García, I. ., Lucena-González, M. J., Martín-Rodríguez, J. L., Charte Ojeda, F. ., & Herrera, F. . (2021). Slicer: Feature Learning for Class Separability with Least-Squares Support Vector Machine Loss and COVID-19 Chest X-Ray Case Study. In H. S. González, I. P. López, P. G. Bringas, H. . Quintián, & E. . Corchado (Eds.), Hybrid Artificial Intelligent Systems (HAIS 2021) (pp. 305-315). Cham: Springer International Publishing.

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

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

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, 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)

Charte, D. ", Charte Ojeda, F. ., García López, S. ., & Herrera Triguero, F. . (2018). A snapshot on nonstandard supervised learning problems: taxonomy, relationships, problem transformations and algorithm adaptations. Progress in Artificial Intelligence. https://doi.org/10.1007/s13748-018-00167-7 (Original work published 2025)

Charte Ojeda, F. ., Rivera Rivas, A. J. ., Charte, D. ", del Jesus Díaz, M. J. ., & Herrera Triguero, F. . (2018). Tips, guidelines and tools for managing multi-label datasets: The mldr.datasets R package and the Cometa data repository. Neurocomputing, 289, 68-85. https://doi.org/10.1016/j.neucom.2018.02.011

Charte, D. ", Charte Ojeda, F. ., García López, S. ., del Jesus Díaz, M. J. ., & Herrera Triguero, F. . (2018). A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines. 949-950. Granada (Spain). (Original work published)