Working with Multilabel Datasets in R: The mldr Package. - Francisco Charte; David Charte (R J., 2015)

R Ultimate Multilabel Dataset Repository. - Francisco Charte; David Charte; Antonio J. Rivera; María José del Jesus; Francisco Herrera (HAIS, 2016)

A snapshot on nonstandard supervised learning problems: taxonomy, relationships and methods. - David Charte; Francisco Charte; Salvador García 001; Francisco Herrera (CoRR, 2018)

Tips, guidelines and tools for managing multi-label datasets: The mldr.datasets R package and the Cometa data repository. - Francisco Charte; Antonio J. Rivera; David Charte; María José del Jesus; Francisco Herrera (Neurocomputing, 2018)

A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines. - David Charte; Francisco Charte; Salvador García 001; María José del Jesus; Francisco Herrera (Inf. Fusion, 2018)

A Showcase of the Use of Autoencoders in Feature Learning Applications. - David Charte; Francisco Charte; María José del Jesus; Francisco Herrera (IWINAC (2), 2019)

Ruta: Implementations of neural autoencoders in R. - David Charte; Francisco Herrera; Francisco Charte (Knowl. Based Syst., 2019)

A snapshot on nonstandard supervised learning problems: taxonomy, relationships, problem transformations and algorithm adaptations. - David Charte; Francisco Charte; Salvador García 001; Francisco Herrera (Prog. Artif. Intell., 2019)

An analysis on the use of autoencoders for representation learning: Fundamentals, learning task case studies, explainability and challenges. - David Charte; Francisco Charte; María José del Jesus; Francisco Herrera (Neurocomputing, 2020)

Artificial intelligence within the interplay between natural and artificial computation: Advances in data science, trends and applications. - Juan Manuel Górriz; Javier Ramírez 001; Andrés Ortiz 001; Francisco Jesús Martínez-Murcia; Fermín Segovia; John Suckling; Matthew Leming; Yu-Dong Zhang 001; José Ramón Álvarez Sánchez; Guido Bologna; María Paula Bonomini; Fernando E. Casado; et al. (Neurocomputing, 2020)

COVIDGR Dataset and COVID-SDNet Methodology for Predicting COVID-19 Based on Chest X-Ray Images. - Siham Tabik; Anabel Gómez-Ríos; J. L. Martín-Rodríguez; I. Sevillano-García; Manuel Rey-Area; David Charte; Emilio Guirado; Juan-Luis Suárez; Julián Luengo; M. A. Valero-González; P. García-Villanova; Eulalia Olmedo-Sánchez; et al. (IEEE J. Biomed. Health Informatics, 2020)

Slicer: Feature Learning for Class Separability with Least-Squares Support Vector Machine Loss and COVID-19 Chest X-Ray Case Study. - David Charte; Iván Sevillano-García; María Jesús Lucena-González; José Luis Martín-Rodríguez; Francisco Charte; Francisco Herrera (HAIS, 2021)

Revisiting data complexity metrics based on morphology for overlap and imbalance: snapshot, new overlap number of balls metrics and singular problems prospect. - José Daniel Pascual-Triana; David Charte; Marta Andrés Arroyo; Alberto Fernández 001; Francisco Herrera (Knowl. Inf. Syst., 2021)

A tutorial on the segmentation of metallographic images: Taxonomy, new MetalDAM dataset, deep learning-based ensemble model, experimental analysis and challenges. - Julián Luengo; Raúl Moreno; Iván Sevillano-García; David Charte; Adrián Peláez-Vegas; Marta Fernández-Moreno; Pablo Mesejo; Francisco Herrera (Inf. Fusion, 2022)

Reducing Data Complexity Using Autoencoders With Class-Informed Loss Functions. - David Charte; Francisco Charte; Francisco Herrera (IEEE Trans. Pattern Anal. Mach. Intell., 2022)