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Antonio J. Tallón-Ballesteros
Researcher at University of Huelva
Publications - 50
Citations - 338
Antonio J. Tallón-Ballesteros is an academic researcher from University of Huelva. The author has contributed to research in topics: Feature selection & Artificial neural network. The author has an hindex of 10, co-authored 47 publications receiving 250 citations. Previous affiliations of Antonio J. Tallón-Ballesteros include University of Seville & Pablo de Olavide University.
Papers
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Journal ArticleDOI
Predicting concentration levels of air pollutants by transfer learning and recurrent neural network
TL;DR: Experimentation has shown that LSTM RNNs initialized with transfer learning methods have higher prediction accuracy; it incurred shorter training time than randomly initialized recurrent neural networks.
Book ChapterDOI
Data Mining Methods Applied to a Digital Forensics Task for Supervised Machine Learning
TL;DR: This chapter performs an experimental study on a forensics data task for multi-class classification including several types of methods such as decision trees, bayes classifiers, based on rules, artificial neural networks and based on nearest neighbors.
Journal ArticleDOI
Massive missing data reconstruction in ocean buoys with evolutionary product unit neural networks
Antonio Manuel Durán-Rosal,César Hervás-Martínez,Antonio J. Tallón-Ballesteros,A. C. Martínez-Estudillo,Sancho Salcedo-Sanz +4 more
TL;DR: This work shows the potential of EPUNN to obtain simple, interpretable models in spite of the non-linear characteristic of the neural network, much simpler than the commonly used sigmoid-based neural systems.
Journal ArticleDOI
A two-stage algorithm in evolutionary product unit neural networks for classification
TL;DR: The proposed procedure to add broader diversity at the beginning of the evolutionary process consists of creating two initial populations with different parameter settings, evolving them for a small number of generations, selecting the best individuals from each population in the same proportion and combining them to constitute a new initial population.
Journal ArticleDOI
Feature selection to enhance a two-stage evolutionary algorithm in product unit neural networks for complex classification problems
TL;DR: In this paper, feature selection methods were combined with a two-stage evolutionary classifier based on product unit neural networks for liver transplantation real-world problem with serious troubles in the data distribution and classifiers get low performance.