| COVIDGR dataset and COVID-SDNet methodology for predicting COVID-19 based on chest X-ray images S Tabik, A Gómez-Ríos, JL Martín-Rodríguez, I Sevillano-García, ... IEEE journal of biomedical and health informatics 24 (12), 3595-3605, 2020 | 400 | 2020 |
| A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines D Charte, F Charte, S García, MJ del Jesus, F Herrera Information Fusion 44 (November 2018), 78-96, 2017 | 349 | 2017 |
| Artificial intelligence within the interplay between natural and artificial computation: Advances in data science, trends and applications JM Górriz, J Ramírez, A Ortiz, FJ Martinez-Murcia, F Segovia, J Suckling, ... Neurocomputing 410, 237-270, 2020 | 246 | 2020 |
| Working with Multilabel Datasets in R: The mldr Package F Charte, FD Charte The R Journal 7 (2), 149--162, 2015 | 87 | 2015 |
| An analysis on the use of autoencoders for representation learning: Fundamentals, learning task case studies, explainability and challenges D Charte, F Charte, MJ del Jesus, F Herrera Neurocomputing 404, 93-107, 2020 | 72 | 2020 |
| A tutorial on the segmentation of metallographic images: Taxonomy, new MetalDAM dataset, deep learning-based ensemble model, experimental analysis and challenges J Luengo, R Moreno, I Sevillano, D Charte, A Pelaez-Vegas, ... Information Fusion 78, 232-253, 2022 | 36 | 2022 |
| A snapshot on nonstandard supervised learning problems: taxonomy, relationships, problem transformations and algorithm adaptations D Charte, F Charte, S García, F Herrera Progress in Artificial Intelligence 8 (1), 1-14, 2018 | 36 | 2018 |
| Tips, guidelines and tools for managing multi-label datasets: The mldr. datasets R package and the Cometa data repository F Charte, AJ Rivera, D Charte, MJ del Jesus, F Herrera Neurocomputing 289, 68-85, 2018 | 32 | 2018 |
| R ultimate multilabel dataset repository F Charte, D Charte, A Rivera, MJ del Jesus, F Herrera Hybrid Artificial Intelligent Systems: 11th International Conference, HAIS …, 2016 | 32 | 2016 |
| Revisiting Data Complexity Metrics Based on Morphology for Overlap and Imbalance: Snapshot, New Overlap Number of Balls Metrics and Singular Problems Prospect JD Pascual-Triana, D Charte, MA Arroyo, A Fernández, F Herrera Knowledge and Information Systems, 2021 | 21 | 2021 |
| Reducing Data Complexity using Autoencoders with Class-informed Loss Functions D Charte, F Charte, F Herrera IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021 | 19 | 2021 |
| Ruta: Implementations of neural autoencoders in R D Charte, F Herrera, F Charte Knowledge-Based Systems 174, 4 - 8, 2019 | 9 | 2019 |
| A showcase of the use of autoencoders in feature learning applications D Charte, F Charte, MJ del Jesus, F Herrera International Work-Conference on the Interplay Between Natural and …, 2019 | 7 | 2019 |
| Slicer: feature learning for class separability with least-squares support vector machine loss and COVID-19 chest X-ray case study D Charte, I Sevillano-García, MJ Lucena-González, JL Martín-Rodríguez, ... Hybrid Artificial Intelligent Systems: 16th International Conference, HAIS …, 2021 | 3 | 2021 |
| How to work with multilabel datasets in R using the mldr package F Charte, FD Charte Figshare, 2015 | 2 | 2015 |
| mldr: Paquete R para Exploración de Datos Multietiqueta D Charte, F Charte Proc. 16th Conferencia de la Asociación Española Para la Inteligencia …, 2015 | 1 | 2015 |
| Machine Learning y Ciencia de Datos con Python y R F Charte, D Charte Krasis Press - 978-84-945822-5-7, 2021 | | 2021 |
Francisco HerreraProfessor Computer Science and AI, DaSCI Research Institute, Granada University, Spain在 decsai.ugr.es 的电子邮件经过验证
