Francisco Charte

Publication Activity (10 Years)

Years Active: 2011-2024
Publications (10 Years): 47

Top Topics

Multilabel Classification

Top Venues

Publications

María Dolores Pérez-Godoy, Marta Molina, Francisco Martínez, David A. Elizondo, Francisco Charte, Antonio J. Rivera
DESReg: Dynamic Ensemble Selection library for Regression tasks. Neurocomputing 580 (2024)
Francisco Charte, Antonio J. Rivera, Francisco Martínez, María José del Jesus
EvoAAA: An evolutionary methodology for automated \neural autoencoder architecture search. CoRR (2023)
Antonio J. Rivera, Miguel A. Dávila, David A. Elizondo, María José del Jesus, Francisco Charte
mldr.resampling: Efficient reference implementations of multilabel resampling algorithms. Neurocomputing 559 (2023)
M. I. Cabrera-Bermejo, María José del Jesus, Antonio J. Rivera, David A. Elizondo, Francisco Charte, M. Dolores Pérez-Godoy
Analysis of Transformer Model Applications. HAIS (2023)
David de la Rosa, Antonio J. Rivera, María José del Jesus, Francisco Charte
PARDINUS: Weakly supervised discarding of photo-trapping empty images based on autoencoders. CoRR (2023)
Antonio J. Rivera, Miguel A. Dávila, David A. Elizondo, María José del Jesus, Francisco Charte
mldr.resampling: Efficient Reference Implementations of Multilabel Resampling Algorithms. CoRR (2023)
David de la Rosa, Antonio Álvarez, Ramón Pérez, Germán Garrote, Antonio J. Rivera, María José del Jesus, Francisco Charte
NOSpcimen: A First Approach to Unsupervised Discarding of Empty Photo Trap Images. IWANN (2) (2023)
Antonio Jesús Rivera, J. Cobo Muñoz, M. D. Pérez-Goody, B. Sáenz de San Pedro, Francisco Charte, David A. Elizondo, C. Rodríguez, M. L. Abolafia, A. Perea, María José del Jesus
XAIRE: An ensemble-based methodology for determining the relative importance of variables in regression tasks. Application to a hospital emergency department. Artif. Intell. Medicine 137 (2023)
Francisco Martínez, Francisco Charte, María Pilar Frías, Ana María Martínez-Rodríguez
Strategies for time series forecasting with generalized regression neural networks. Neurocomputing 491 (2022)
David Charte, Francisco Charte, Francisco Herrera
Reducing Data Complexity Using Autoencoders With Class-Informed Loss Functions. IEEE Trans. Pattern Anal. Mach. Intell. 44 (12) (2022)
David Charte, Francisco Charte, Francisco Herrera
Reducing Data Complexity using Autoencoders with Class-informed Loss Functions. CoRR (2021)
Francisco Javier Pulgar, Francisco Charte, Antonio J. Rivera, María José del Jesus
ClEnDAE: A classifier based on ensembles with built-in dimensionality reduction through denoising autoencoders. Inf. Sci. 565 (2021)
David Charte, Iván Sevillano-García, María Jesús Lucena-González, José Luis Martín-Rodríguez, Francisco Charte, Francisco Herrera
Slicer: Feature Learning for Class Separability with Least-Squares Support Vector Machine Loss and COVID-19 Chest X-Ray Case Study. HAIS (2021)
Ángel Miguel García-Vico, Francisco Charte, Pedro González, David A. Elizondo, Cristóbal José Carmona
E2PAMEA: A fast evolutionary algorithm for extracting fuzzy emerging patterns in big data environments. Neurocomputing 415 (2020)
Francisco Javier Pulgar, Francisco Charte, Antonio J. Rivera, María José del Jesus
Choosing the proper autoencoder for feature fusion based on data complexity and classifiers: Analysis, tips and guidelines. Inf. Fusion 54 (2020)
David Charte, Francisco Charte, María José del Jesus, Francisco Herrera
An analysis on the use of autoencoders for representation learning: fundamentals, learning task case studies, explainability and challenges. CoRR (2020)
Juan Manuel Górriz, Javier Ramírez, Andrés Ortiz, Francisco Jesús Martínez-Murcia, Fermín Segovia, John Suckling, Matthew Leming, Yu-Dong Zhang, José Ramón Álvarez Sánchez, Guido Bologna, María Paula Bonomini, Fernando E. Casado, David Charte, Francisco Charte, Ricardo Contreras, Alfredo Cuesta-Infante, Richard J. Duro, Antonio Fernández-Caballero, José Manuel Ferrández
Artificial intelligence within the interplay between natural and artificial computation: Advances in data science, trends and applications. Neurocomputing 410 (2020)
David Charte, Francisco Charte, María José del Jesus, Francisco Herrera
A Showcase of the Use of Autoencoders in Feature Learning Applications. CoRR (2020)
Francisco Charte
A Comprehensive and Didactic Review on Multilabel Learning Software Tools. IEEE Access 8 (2020)
Francisco Charte, Antonio J. Rivera, Francisco Martínez, María José del Jesus
EvoAAA: An evolutionary methodology for automated neural autoencoder architecture search. Integr. Comput. Aided Eng. 27 (3) (2020)
David Charte, Francisco Charte, María José del Jesus, Francisco Herrera
An analysis on the use of autoencoders for representation learning: Fundamentals, learning task case studies, explainability and challenges. Neurocomputing 404 (2020)
Francisco Charte, Antonio J. Rivera, María José del Jesus, Francisco Herrera
Dealing with difficult minority labels in imbalanced mutilabel data sets. Neurocomputing (2019)
David Charte, Francisco Charte, Salvador García, Francisco Herrera
A snapshot on nonstandard supervised learning problems: taxonomy, relationships, problem transformations and algorithm adaptations. Prog. Artif. Intell. 8 (1) (2019)
Francisco Charte, Antonio J. Rivera, María José del Jesus, Francisco Herrera
REMEDIAL-HwR: Tackling multilabel imbalance through label decoupling and data resampling hybridization. Neurocomputing (2019)
Francisco Martínez, María Pilar Frías, Francisco Charte, Antonio J. Rivera
Time Series Forecasting with KNN in R: the tsfknn Package. R J. 11 (2) (2019)
Francisco Javier Pulgar, Francisco Charte, Antonio J. Rivera, María José del Jesus
AEkNN: An AutoEncoder kNN-Based Classifier With Built-in Dimensionality Reduction. Int. J. Comput. Intell. Syst. 12 (1) (2019)
David Charte, Francisco Charte, María José del Jesus, Francisco Herrera
A Showcase of the Use of Autoencoders in Feature Learning Applications. IWINAC (2) (2019)
David Charte, Francisco Herrera, Francisco Charte
Ruta: Implementations of neural autoencoders in R. Knowl. Based Syst. 174 (2019)
Francisco Charte, Alberto Vico, María Dolores Pérez-Godoy, Antonio J. Rivera
predtoolsTS: R package for streamlining time series forecasting. Prog. Artif. Intell. 8 (4) (2019)
Francisco Martínez, Francisco Charte, Antonio J. Rivera, María Pilar Frías
Automatic Time Series Forecasting with GRNN: A Comparison with Other Models. IWANN (1) (2019)
Francisco Charte, Antonio J. Rivera, Francisco Martínez, María José del Jesus
Automating Autoencoder Architecture Configuration: An Evolutionary Approach. IWINAC (1) (2019)
Ignacio Cordón, Julián Luengo, Salvador García, Francisco Herrera, Francisco Charte
: Data preprocessing to achieve smart data in R. Neurocomputing 360 (2019)
David Charte, Francisco Charte, Salvador García, Francisco Herrera
A snapshot on nonstandard supervised learning problems: taxonomy, relationships and methods. CoRR (2018)
David Charte, Francisco Charte, Salvador García, María José del Jesus, Francisco Herrera
A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines. Inf. Fusion 44 (2018)
Francisco Charte, Antonio J. Rivera, David Charte, María José del Jesus, Francisco Herrera
Tips, guidelines and tools for managing multi-label datasets: The mldr.datasets R package and the Cometa data repository. Neurocomputing 289 (2018)
Francisco Charte, Antonio J. Rivera, María José del Jesus, Francisco Herrera
Tackling Multilabel Imbalance through Label Decoupling and Data Resampling Hybridization. CoRR (2018)
Francisco Charte, Antonio J. Rivera, David Charte, María José del Jesus, Francisco Herrera
Tips, guidelines and tools for managing multi-label datasets: the mldr.datasets R package and the Cometa data repository. CoRR (2018)
Francisco Javier Pulgar, Francisco Charte, Antonio J. Rivera, María José del Jesus
A First Approach to Face Dimensionality Reduction Through Denoising Autoencoders. IDEAL (1) (2018)
Francisco Javier Pulgar, Francisco Charte, Antonio J. Rivera, María José del Jesus
AEkNN: An AutoEncoder kNN-based classifier with built-in dimensionality reduction. CoRR (2018)
Francisco Charte, Antonio J. Rivera, María José del Jesus, Francisco Herrera
Dealing with Difficult Minority Labels in Imbalanced Mutilabel Data Sets. CoRR (2018)
David Charte, Francisco Charte, Salvador García, María José del Jesus, Francisco Herrera
A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines. CoRR (2018)
Francisco Charte, Inmaculada Romero, María Dolores Pérez-Godoy, Antonio J. Rivera, Eulogio Castro
Comparative analysis of data mining and response surface methodology predictive models for enzymatic hydrolysis of pretreated olive tree biomass. Comput. Chem. Eng. 101 (2017)
Francisco Javier Pulgar, Antonio J. Rivera, Francisco Charte, María José del Jesus
On the Impact of Imbalanced Data in Convolutional Neural Networks Performance. HAIS (2017)
Ronaldo C. Prati, Francisco Charte, Francisco Herrera
A first approach towards a fuzzy decision tree for multilabel classification. FUZZ-IEEE (2017)
Francisco Charte, Antonio J. Rivera, María José del Jesus, Francisco Herrera
On the Impact of Dataset Complexity and Sampling Strategy in Multilabel Classifiers Performance. HAIS (2016)
Ángel M. García, Francisco Charte, Pedro González, Cristóbal J. Carmona, María José del Jesus
Subgroup Discovery with Evolutionary Fuzzy Systems in R: The SDEFSR Package. R J. 8 (2) (2016)
Francisco Charte, David Charte, Antonio J. Rivera, María José del Jesus, Francisco Herrera
R Ultimate Multilabel Dataset Repository. HAIS (2016)
Francisco Charte, Antonio Jesús Rivera, María José del Jesus, Francisco Herrera
Resampling Multilabel Datasets by Decoupling Highly Imbalanced Labels. HAIS (2015)
Francisco Charte, Antonio J. Rivera, María José del Jesus, Francisco Herrera
Addressing imbalance in multilabel classification: Measures and random resampling algorithms. Neurocomputing 163 (2015)
Francisco Charte, Antonio J. Rivera, María José del Jesus, Francisco Herrera
QUINTA: A question tagging assistant to improve the answering ratio in electronic forums. EUROCON (2015)
Francisco Charte, David Charte
Working with Multilabel Datasets in R: The mldr Package. R J. 7 (2) (2015)
Francisco Charte, Antonio J. Rivera, María José del Jesus, Francisco Herrera
MLSMOTE: Approaching imbalanced multilabel learning through synthetic instance generation. Knowl. Based Syst. 89 (2015)
María Dolores Pérez-Godoy, Antonio Jesús Rivera, Francisco Charte, María José del Jesus
CO ^2 RBFN-CS: First Approach Introducing Cost-Sensitivity in the Cooperative-Competitive RBFN Design. IWANN (1) (2015)
Francisco Charte, Antonio J. Rivera, María José del Jesus, Francisco Herrera
MLeNN: A First Approach to Heuristic Multilabel Undersampling. IDEAL (2014)
Francisco Charte, Antonio J. Rivera, María José del Jesus, Francisco Herrera
LI-MLC: A Label Inference Methodology for Addressing High Dimensionality in the Label Space for Multilabel Classification. IEEE Trans. Neural Networks Learn. Syst. 25 (10) (2014)
Francisco Charte, Antonio Jesús Rivera Rivas, María José del Jesus, Francisco Herrera
Concurrence among Imbalanced Labels and Its Influence on Multilabel Resampling Algorithms. HAIS (2014)
Francisco Charte, Antonio Jesús Rivera Rivas, María José del Jesus, Francisco Herrera
A First Approach to Deal with Imbalance in Multi-label Datasets. HAIS (2013)
Francisco Charte, Antonio Jesús Rivera Rivas, María José del Jesus, Francisco Herrera
Improving Multi-label Classifiers via Label Reduction with Association Rules. HAIS (2) (2012)
Antonio Jesús Rivera Rivas, Francisco Charte, M. Dolores Pérez-Godoy, María José del Jesus
RBFN: A First Approach to the Problem Transformation Methodology for Multi-label Classification. IWANN (1) (2011)