Multi-label Testing for CO2RBFN: A First Approach to the Problem Transformation Methodology for Multi-label Classification. - Antonio Jesús Rivera Rivas; Francisco Charte; M. Dolores Pérez-Godoy; María José del Jesus (IWANN (1), 2011)

Improving Multi-label Classifiers via Label Reduction with Association Rules. - Francisco Charte; Antonio Jesús Rivera Rivas; María José del Jesus; Francisco Herrera (HAIS (2), 2012)

A First Approach to Deal with Imbalance in Multi-label Datasets. - Francisco Charte; Antonio Jesús Rivera Rivas; María José del Jesus; Francisco Herrera (HAIS, 2013)

Alternative OVA Proposals for Cooperative Competitive RBFN Design in Classification Tasks. - Francisco Charte Ojeda; Antonio Jesús Rivera Rivas; María Dolores Pérez-Godoy; María José del Jesus (IWANN (1), 2013)

Concurrence among Imbalanced Labels and Its Influence on Multilabel Resampling Algorithms. - Francisco Charte; Antonio Jesús Rivera Rivas; María José del Jesus; Francisco Herrera (HAIS, 2014)

MLeNN: A First Approach to Heuristic Multilabel Undersampling. - Francisco Charte; Antonio J. Rivera; María José del Jesus; Francisco Herrera (IDEAL, 2014)

LI-MLC: A Label Inference Methodology for Addressing High Dimensionality in the Label Space for Multilabel Classification. - Francisco Charte; Antonio J. Rivera; María José del Jesus; Francisco Herrera (IEEE Trans. Neural Networks Learn. Syst., 2014)

QUINTA: A question tagging assistant to improve the answering ratio in electronic forums. - Francisco Charte; Antonio J. Rivera; María José del Jesus; Francisco Herrera (EUROCON, 2015)

Resampling Multilabel Datasets by Decoupling Highly Imbalanced Labels. - Francisco Charte; Antonio Jesús Rivera; María José del Jesus; Francisco Herrera (HAIS, 2015)

CO ^2 RBFN-CS: First Approach Introducing Cost-Sensitivity in the Cooperative-Competitive RBFN Design. - María Dolores Pérez-Godoy; Antonio Jesús Rivera; Francisco Charte; María José del Jesus (IWANN (1), 2015)

Addressing imbalance in multilabel classification: Measures and random resampling algorithms. - Francisco Charte; Antonio J. Rivera; María José del Jesus; Francisco Herrera (Neurocomputing, 2015)

MLSMOTE: Approaching imbalanced multilabel learning through synthetic instance generation. - Francisco Charte; Antonio J. Rivera; María José del Jesus; Francisco Herrera (Knowl. Based Syst., 2015)

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

Multilabel Classification - Problem Analysis, Metrics and Techniques - Francisco Herrera; Francisco Charte; Antonio J. Rivera; María José del Jesus (Springer, 2016)

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

On the Impact of Dataset Complexity and Sampling Strategy in Multilabel Classifiers Performance. - Francisco Charte; Antonio J. Rivera; María José del Jesus; Francisco Herrera (HAIS, 2016)

Subgroup Discovery with Evolutionary Fuzzy Systems in R: The SDEFSR Package. - Ángel M. García; Francisco Charte; Pedro González 001; Cristóbal J. Carmona; María José del Jesus (R J., 2016)

A first approach towards a fuzzy decision tree for multilabel classification. - Ronaldo C. Prati; Francisco Charte; Francisco Herrera (FUZZ-IEEE, 2017)

On the Impact of Imbalanced Data in Convolutional Neural Networks Performance. - Francisco Javier Pulgar; Antonio J. Rivera; Francisco Charte; María José del Jesus (HAIS, 2017)

Modeling the Transformation of Olive Tree Biomass into Bioethanol with Reg-CO ^2 RBFN. - Francisco Charte Ojeda; Inmaculada Romero; Antonio Jesús Rivera Rivas; Eulogio Castro (IWANN (1), 2017)

A Transformation Approach Towards Big Data Multilabel Decision Trees. - Antonio Jesús Rivera Rivas; Francisco Charte Ojeda; Francisco Javier Pulgar; María José del Jesus (IWANN (1), 2017)

Comparative analysis of data mining and response surface methodology predictive models for enzymatic hydrolysis of pretreated olive tree biomass. - Francisco Charte; Inmaculada Romero; María Dolores Pérez-Godoy; Antonio J. Rivera; Eulogio Castro (Comput. Chem. Eng., 2017)

A First Approach to Face Dimensionality Reduction Through Denoising Autoencoders. - Francisco Javier Pulgar; Francisco Charte; Antonio J. Rivera; María José del Jesus (IDEAL (1), 2018)

An Approximation to Deep Learning Touristic-Related Time Series Forecasting. - Daniel Trujillo Viedma; Antonio Jesús Rivera Rivas; Francisco Charte Ojeda; María José del Jesus Díaz (IDEAL (1), 2018)

Tackling Multilabel Imbalance through Label Decoupling and Data Resampling Hybridization. - Francisco Charte; Antonio J. Rivera; María José del Jesus; Francisco Herrera (CoRR, 2018)

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)

Automatic Time Series Forecasting with GRNN: A Comparison with Other Models. - Francisco Martínez; Francisco Charte; Antonio J. Rivera; María Pilar Frías (IWANN (1), 2019)

A First Approximation to the Effects of Classical Time Series Preprocessing Methods on LSTM Accuracy. - Daniel Trujillo Viedma; Antonio Jesús Rivera Rivas; Francisco Charte Ojeda; María José del Jesus Díaz (IWANN (1), 2019)

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

Automating Autoencoder Architecture Configuration: An Evolutionary Approach. - Francisco Charte; Antonio J. Rivera; Francisco Martínez; María José del Jesus (IWINAC (1), 2019)

AEkNN: An AutoEncoder kNN-Based Classifier With Built-in Dimensionality Reduction. - Francisco Javier Pulgar; Francisco Charte; Antonio J. Rivera; María José del Jesus (Int. J. Comput. Intell. Syst., 2019)

Dealing with difficult minority labels in imbalanced mutilabel data sets. - Francisco Charte; Antonio J. Rivera; María José del Jesus; Francisco Herrera (Neurocomputing, 2019)

REMEDIAL-HwR: Tackling multilabel imbalance through label decoupling and data resampling hybridization. - Francisco Charte; Antonio J. Rivera; María José del Jesus; Francisco Herrera (Neurocomputing, 2019)

Smartdata: Data preprocessing to achieve smart data in R. - Ignacio Cordón; Julián Luengo; Salvador García 001; Francisco Herrera; Francisco Charte (Neurocomputing, 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)

predtoolsTS: R package for streamlining time series forecasting. - Francisco Charte; Alberto Vico; María Dolores Pérez-Godoy; Antonio J. Rivera (Prog. Artif. Intell., 2019)

Time Series Forecasting with KNN in R: the tsfknn Package. - Francisco Martínez; María Pilar Frías; Francisco Charte; Antonio J. Rivera (R J., 2019)

A Comprehensive and Didactic Review on Multilabel Learning Software Tools. - Francisco Charte (IEEE Access, 2020)

EvoAAA: An evolutionary methodology for automated neural autoencoder architecture search. - Francisco Charte; Antonio J. Rivera; Francisco Martínez; María José del Jesus (Integr. Comput. Aided Eng., 2020)

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)

E2PAMEA: A fast evolutionary algorithm for extracting fuzzy emerging patterns in big data environments. - Ángel Miguel García-Vico; Francisco Charte; Pedro González 001; David A. Elizondo; Cristóbal José Carmona (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)

Choosing the proper autoencoder for feature fusion based on data complexity and classifiers: Analysis, tips and guidelines. - Francisco Javier Pulgar; Francisco Charte; Antonio J. Rivera; María José del Jesus (Inf. Fusion, 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)

ClEnDAE: A classifier based on ensembles with built-in dimensionality reduction through denoising autoencoders. - Francisco Javier Pulgar; Francisco Charte; Antonio J. Rivera; María José del Jesus (Inf. Sci., 2021)

Strategies for time series forecasting with generalized regression neural networks. - Francisco Martínez; Francisco Charte; María Pilar Frías; Ana María Martínez-Rodríguez (Neurocomputing, 2022)

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

Analysis of Transformer Model Applications. - Maria Isabel Cabrera-Bermejo; María José del Jesus; Antonio J. Rivera; David A. Elizondo; Francisco Charte; M. Dolores Pérez-Godoy (HAIS, 2023)

NOSpcimen: A First Approach to Unsupervised Discarding of Empty Photo Trap Images. - David de la Rosa; Antonio Álvarez; Ramón Pérez; Germán Garrote; Antonio J. Rivera; María José del Jesus; Francisco Charte (IWANN (2), 2023)

PARDINUS: Weakly supervised discarding of photo-trapping empty images based on autoencoders. - David de la Rosa; Antonio J. Rivera; María José del Jesus; Francisco Charte (CoRR, 2023)

XAIRE: An ensemble-based methodology for determining the relative importance of variables in regression tasks. Application to a hospital emergency department. - Antonio Jesús Rivera; J. Cobo Muñoz; M. Dolores Pérez-Godoy; B. Sáenz de San Pedro; Francisco Charte; David A. Elizondo; C. Rodríguez; M. L. Abolafia; A. Perea; María José del Jesus (Artif. Intell. Medicine, 2023)

mldr.resampling: Efficient reference implementations of multilabel resampling algorithms. - Antonio J. Rivera; Miguel A. Dávila; David A. Elizondo; María José del Jesus; Francisco Charte (Neurocomputing, 2023)

DESReg: Dynamic Ensemble Selection library for Regression tasks. - María Dolores Pérez-Godoy; Marta Molina; Francisco Martínez; David A. Elizondo; Francisco Charte; Antonio J. Rivera (Neurocomputing, 2024)

MORENA: Empty Images Detection Based on Unsupervised Reconstruction Error Analysis. - David de la Rosa; María José del Jesus; María Dolores Pérez-Godoy; Francisco Charte (IWANN (1), 2025)

Addressing Multilabel Imbalance with an Efficiency-Focused Approach Using Diffusion Model-Generated Synthetic Samples. - Francisco Charte; Miguel A. Dávila; María Dolores Pérez-Godoy; María José del Jesus (CoRR, 2025)