Rivera, A. J. (0000-0002-1062-3127)

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Antonio J. Rivera received the B.Sc. and Ph.D. degrees in computer science from the University of Granada, Granada, Spain, in 1995 and 2003, respectively. He is a Lecturer of computer architecture and computer technology with the Computer Science Department, University of Jaén, Jaén, Spain. His current research interests include multilabel classification, imbalance problems, evolutionary computation, neural network design, deep learning, time series prediction, and regression tasks.

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Universidad de Jaén: Jaén, Andalucía, ES

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Rivera, A. J.

https://orcid.org/0000-0002-1062-3127

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Universidad de Jaén: Jaén, Andalucía, ES

Works (50 of 61)

Nets4Learning: A Web Platform for Designing and Testing ANN/DNN Models

DESReg: Dynamic Ensemble Selection library for Regression tasks

Analysis of Transformer Model Applications

NOSpcimen: A First Approach to Unsupervised Discarding of Empty Photo Trap Images

XAIRE: An ensemble-based methodology for determining the relative importance of variables in regression tasks. Application to a hospital emergency department

Time Series Forecasting by Generalized Regression Neural Networks Trained With Multiple Series

Choosing the proper autoencoder for feature fusion based on data complexity and classifiers: Analysis, tips and guidelines

A First Approximation to the Effects of Classical Time Series Preprocessing Methods on LSTM Accuracy

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

Automating Autoencoder Architecture Configuration: An Evolutionary Approach

Dealing with difficult minority labels in imbalanced mutilabel data sets

predtoolsTS: R package for streamlining time series forecasting

REMEDIAL-HwR: Tackling multilabel imbalance through label decoupling and data resampling hybridization

A methodology for applying k-nearest neighbor to time series forecasting

A First Approach to Face Dimensionality Reduction Through Denoising Autoencoders

AEkNN: An autoencoder kNN–based classifier with built-in dimensionality reduction

Dealing with seasonality by narrowing the training set in time series forecasting with kNN

Tips, guidelines and tools for managing multi-label datasets: The mldr.datasets R package and the Cometa data repository

Comparative analysis of data mining and response surface methodology predictive models for enzymatic hydrolysis of pretreated olive tree biomass

MEFASD-BD: Multi-objective evolutionary fuzzy algorithm for subgroup discovery in big data environments - A MapReduce solution

On the impact of imbalanced data in convolutional neural networks performance

Estimating the maximum power delivered by concentrating photovoltaics technology through atmospheric conditions using a differential evolution approach

Multilabel classification: Problem analysis, metrics and techniques

On the impact of dataset complexity and sampling strategy in multilabel classifiers performance

R ultimate multilabel dataset repository

Recognition of activities in resource constrained environments; reducing the computational complexity

A differential evolution proposal for estimating the maximum power delivered by CPV modules under real outdoor conditions

Addressing imbalance in multilabel classification: Measures and random resampling algorithms

Co&lt;sup&gt;2&lt;/sup&gt;RBFN-CS: First approach introducing cost-sensitivity in the cooperative-competitive RBFN design

MLSMOTE: Approaching imbalanced multilabel learning through synthetic instance generation

QUINTA: A question tagging assistant to improve the answering ratio in electronic forums

Resampling multilabel datasets by decoupling highly imbalanced labels

Resampling multilabel datasets by decoupling highly imbalanced labels

Concurrence among imbalanced labels and its influence on multilabel resampling algorithms

LI-MLC: A label inference methodology for addressing high dimensionality in the label space for multilabel classification

MLeNN: A first approach to heuristic multilabel undersampling

MLeNN: A first approach to heuristic multilabel undersampling

Training algorithms for Radial Basis Function Networks to tackle learning processes with imbalanced data-sets

A first analysis of the effect of local and global optimization weights methods in the cooperative-competitive design of RBFN for imbalanced environments

A first approach to deal with imbalance in multi-label datasets

A performance study of concentrating photovoltaic modules using neural networks: An application with CO<sup>2</sup>RBFN

Characterization of Concentrating Photovoltaic modules by cooperative competitive Radial Basis Function Networks

Improving multi-label classifiers via label reduction with association rules

A study on the medium-term forecasting using exogenous variable selection of the extra-virgin olive oil with soft computing methods

A summary on the study of the medium-term forecasting of the extra-virgen olive oil price

Multi-label testing for CO<sup>2</sup>RBFN: A first approach to the problem transformation methodology for multi-label classification

A preliminary study on mutation operators in cooperative competitive algorithms for RBFN design

Analysis of an evolutionary RBFN design algorithm, CO<sup>2</sup>RBFN, for imbalanced data sets

Applying multiobjective RBFNNs optimization and feature selection to a mineral reduction problem

CO<sup>2</sup>RBFN for short and medium term forecasting of the extra-virgin olive oil price

Peer review (13 reviews for 3 publications/grants)

Co<sup>2</sup>RBFN-CS: First approach introducing cost-sensitivity in the cooperative-competitive RBFN design