Francisco Charte

Orcid: 0000-0002-3083-8942

According to our database1, Francisco Charte authored at least 56 papers between 2011 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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Online presence:

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Bibliography

2024
DESReg: Dynamic Ensemble Selection library for Regression tasks.
Neurocomputing, 2024

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

NOSpcimen: A First Approach to Unsupervised Discarding of Empty Photo Trap Images.
Proceedings of the Advances in Computational Intelligence, 2023

Analysis of Transformer Model Applications.
Proceedings of the Hybrid Artificial Intelligent Systems - 18th International Conference, 2023

2022
Reducing Data Complexity Using Autoencoders With Class-Informed Loss Functions.
IEEE Trans. Pattern Anal. Mach. Intell., 2022

Strategies for time series forecasting with generalized regression neural networks.
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

A Comprehensive and Didactic Review on Multilabel Learning Software Tools.
IEEE Access, 2020

2019
Time Series Forecasting with KNN in R: the tsfknn Package.
R J., 2019

predtoolsTS: R package for streamlining time series forecasting.
Prog. Artif. Intell., 2019

A snapshot on nonstandard supervised learning problems: taxonomy, relationships, problem transformations and algorithm adaptations.
Prog. Artif. Intell., 2019

Ruta: Implementations of neural autoencoders in R.
Knowl. Based Syst., 2019

<i>Smartdata</i>: Data preprocessing to achieve smart data in R.
Neurocomputing, 2019

REMEDIAL-HwR: Tackling multilabel imbalance through label decoupling and data resampling hybridization.
Neurocomputing, 2019

Dealing with difficult minority labels in imbalanced mutilabel data sets.
Neurocomputing, 2019

AEkNN: An AutoEncoder kNN-Based Classifier With Built-in Dimensionality Reduction.
Int. J. Comput. Intell. Syst., 2019

Automating Autoencoder Architecture Configuration: An Evolutionary Approach.
Proceedings of the Understanding the Brain Function and Emotions, 2019

A Showcase of the Use of Autoencoders in Feature Learning Applications.
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

Automatic Time Series Forecasting with GRNN: A Comparison with Other Models.
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

An Approximation to Deep Learning Touristic-Related Time Series Forecasting.
Proceedings of the Intelligent Data Engineering and Automated Learning - IDEAL 2018, 2018

A First Approach to Face Dimensionality Reduction Through Denoising Autoencoders.
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

A Transformation Approach Towards Big Data Multilabel Decision Trees.
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

On the Impact of Imbalanced Data in Convolutional Neural Networks Performance.
Proceedings of the Hybrid Artificial Intelligent Systems - 12th International Conference, 2017

A first approach towards a fuzzy decision tree for multilabel classification.
Proceedings of the 2017 IEEE International Conference on Fuzzy Systems, 2017

2016
Subgroup Discovery with Evolutionary Fuzzy Systems in R: The SDEFSR Package.
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

R Ultimate Multilabel Dataset Repository.
Proceedings of the Hybrid Artificial Intelligent Systems - 11th International Conference, 2016

Multilabel Classification - Problem Analysis, Metrics and Techniques
Springer, ISBN: 978-3-319-41111-8, 2016

2015
Working with Multilabel Datasets in R: The mldr Package.
R J., 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

Resampling Multilabel Datasets by Decoupling Highly Imbalanced Labels.
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

MLeNN: A First Approach to Heuristic Multilabel Undersampling.
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

A First Approach to Deal with Imbalance in Multi-label Datasets.
Proceedings of the Hybrid Artificial Intelligent Systems - 8th International Conference, 2013

2012
Improving Multi-label Classifiers via Label Reduction with Association Rules.
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


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