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Improving the Accuracy of a Two-Stage Algorithm in Evolutionary Product Unit Neural Networks for Classification by Means of Feature Selection

Opened Access Improving the Accuracy of a Two-Stage Algorithm in Evolutionary Product Unit Neural Networks for Classification by Means of Feature Selection

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Autor: Tallón Ballesteros, Antonio Javier
Hervás Martínez, César
Riquelme Santos, José Cristóbal
Ruíz, Roberto
Departamento: Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos
Fecha: 2011
Publicado en: New Challenges on Bioinspired Applications Volume 6687 of the series Lecture Notes in Computer Science
ISBN/ISSN: 978-3-642-21325-0
Tipo de documento: Capítulo de Libro
Resumen: This paper introduces a methodology that improves the accuracy of a two-stage algorithm in evolutionary product unit neural networks for classification tasks by means of feature selection. A couple of filters have been taken into consideration to try out the proposal. The experimentation has been carried out on seven data sets from the UCI repository that report test mean accuracy error rates about twenty percent or above with reference classifiers such as C4.5 or 1-NN. The study includes an overall empirical comparison between the models obtained with and without feature selection. Also several classifiers have been tested in order to illustrate the performance of the different filters considered. The results have been contrasted with nonparametric statistical tests and show that our proposal significantly improves the test accuracy of the previous models for the considered data sets. Moreover, the current proposal is much more efficient than a previous methodology deve...
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Tamaño: 141.7Kb
Formato: PDF

URI: http://hdl.handle.net/11441/42174

DOI: http://dx.doi.org/10.1007/978-3-642-21326-7_41

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