Francisco Charte
Orcid: 0000-0002-3083-8942
According to our database1,
Francisco Charte
authored at least 55 papers
between 2011 and 2023.
Collaborative distances:
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Bibliography
2023
mldr.resampling: Efficient reference implementations of multilabel resampling algorithms.
Neurocomputing, November, 2023
XAIRE: An ensemble-based methodology for determining the relative importance of variables in regression tasks. Application to a hospital emergency department.
Artif. Intell. Medicine, March, 2023
PARDINUS: Weakly supervised discarding of photo-trapping empty images based on autoencoders.
CoRR, 2023
Proceedings of the Advances in Computational Intelligence, 2023
Proceedings of the Hybrid Artificial Intelligent Systems - 18th International Conference, 2023
2022
IEEE Trans. Pattern Anal. Mach. Intell., 2022
Neurocomputing, 2022
2021
ClEnDAE: A classifier based on ensembles with built-in dimensionality reduction through denoising autoencoders.
Inf. Sci., 2021
Slicer: Feature Learning for Class Separability with Least-Squares Support Vector Machine Loss and COVID-19 Chest X-Ray Case Study.
Proceedings of the Hybrid Artificial Intelligent Systems - 16th International Conference, 2021
2020
Choosing the proper autoencoder for feature fusion based on data complexity and classifiers: Analysis, tips and guidelines.
Inf. Fusion, 2020
Artificial intelligence within the interplay between natural and artificial computation: Advances in data science, trends and applications.
Neurocomputing, 2020
E2PAMEA: A fast evolutionary algorithm for extracting fuzzy emerging patterns in big data environments.
Neurocomputing, 2020
An analysis on the use of autoencoders for representation learning: Fundamentals, learning task case studies, explainability and challenges.
Neurocomputing, 2020
EvoAAA: An evolutionary methodology for automated neural autoencoder architecture search.
Integr. Comput. Aided Eng., 2020
IEEE Access, 2020
2019
Prog. Artif. Intell., 2019
A snapshot on nonstandard supervised learning problems: taxonomy, relationships, problem transformations and algorithm adaptations.
Prog. Artif. Intell., 2019
Neurocomputing, 2019
REMEDIAL-HwR: Tackling multilabel imbalance through label decoupling and data resampling hybridization.
Neurocomputing, 2019
Neurocomputing, 2019
Int. J. Comput. Intell. Syst., 2019
Proceedings of the Understanding the Brain Function and Emotions, 2019
Proceedings of the From Bioinspired Systems and Biomedical Applications to Machine Learning, 2019
A First Approximation to the Effects of Classical Time Series Preprocessing Methods on LSTM Accuracy.
Proceedings of the Advances in Computational Intelligence, 2019
Proceedings of the Advances in Computational Intelligence, 2019
2018
A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines.
Inf. Fusion, 2018
Tips, guidelines and tools for managing multi-label datasets: The mldr.datasets R package and the Cometa data repository.
Neurocomputing, 2018
A snapshot on nonstandard supervised learning problems: taxonomy, relationships and methods.
CoRR, 2018
Tackling Multilabel Imbalance through Label Decoupling and Data Resampling Hybridization.
CoRR, 2018
Proceedings of the Intelligent Data Engineering and Automated Learning - IDEAL 2018, 2018
Proceedings of the Intelligent Data Engineering and Automated Learning - IDEAL 2018, 2018
2017
Comparative analysis of data mining and response surface methodology predictive models for enzymatic hydrolysis of pretreated olive tree biomass.
Comput. Chem. Eng., 2017
Proceedings of the Advances in Computational Intelligence, 2017
Modeling the Transformation of Olive Tree Biomass into Bioethanol with Reg-CO ^2 RBFN.
Proceedings of the Advances in Computational Intelligence, 2017
Proceedings of the Hybrid Artificial Intelligent Systems - 12th International Conference, 2017
Proceedings of the 2017 IEEE International Conference on Fuzzy Systems, 2017
2016
R J., 2016
On the Impact of Dataset Complexity and Sampling Strategy in Multilabel Classifiers Performance.
Proceedings of the Hybrid Artificial Intelligent Systems - 11th International Conference, 2016
Proceedings of the Hybrid Artificial Intelligent Systems - 11th International Conference, 2016
Springer, ISBN: 978-3-319-41111-8, 2016
2015
MLSMOTE: Approaching imbalanced multilabel learning through synthetic instance generation.
Knowl. Based Syst., 2015
Addressing imbalance in multilabel classification: Measures and random resampling algorithms.
Neurocomputing, 2015
CO ^2 RBFN-CS: First Approach Introducing Cost-Sensitivity in the Cooperative-Competitive RBFN Design.
Proceedings of the Advances in Computational Intelligence, 2015
Proceedings of the Hybrid Artificial Intelligent Systems - 10th International Conference, 2015
QUINTA: A question tagging assistant to improve the answering ratio in electronic forums.
Proceedings of the IEEE EUROCON 2015, 2015
2014
LI-MLC: A Label Inference Methodology for Addressing High Dimensionality in the Label Space for Multilabel Classification.
IEEE Trans. Neural Networks Learn. Syst., 2014
Proceedings of the Intelligent Data Engineering and Automated Learning - IDEAL 2014, 2014
Concurrence among Imbalanced Labels and Its Influence on Multilabel Resampling Algorithms.
Proceedings of the Hybrid Artificial Intelligence Systems - 9th International Conference, 2014
2013
Alternative OVA Proposals for Cooperative Competitive RBFN Design in Classification Tasks.
Proceedings of the Advances in Computational Intelligence, 2013
Proceedings of the Hybrid Artificial Intelligent Systems - 8th International Conference, 2013
2012
Proceedings of the Hybrid Artificial Intelligent Systems - 7th International Conference, 2012
2011
Multi-label Testing for CO<sup>2</sup>RBFN: A First Approach to the Problem Transformation Methodology for Multi-label Classification.
Proceedings of the Advances in Computational Intelligence, 2011