Improving the accuracy of a two-stage algorithm in evolutionary product unit neural networks for classification by means of feature selection

Authors:
Antonio J. Tallón-Ballesteros;César Hervás-Martínez;José C. Riquelme;Roberto Ruiz
Affiliations:
Department of Languages and Computer Systems, University of Seville, Spain;Department of Computer Science and Numerical Analysis, University of Córdoba, Spain;Department of Languages and Computer Systems, University of Seville, Spain;Area of Computer Science, Pablo de Olavide University, Seville, Spain
Venue:
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation: new challenges on bioinspired applications - Volume Part II
Year:
2011

Citing 11
Cited 1

Product units: a computationally powerful and biologically plausible extension to backpropagation networks

Neural Computation
The nature of statistical learning theory

The nature of statistical learning theory
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning

ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Logistic Model Trees

Machine Learning
Pattern Recognition and Machine Learning (Information Science and Statistics)

Pattern Recognition and Machine Learning (Information Science and Statistics)
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)

Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Incremental wrapper-based gene selection from microarray data for cancer classification

Pattern Recognition
Statistical Comparisons of Classifiers over Multiple Data Sets

The Journal of Machine Learning Research
Projection-based measure for efficient feature selection

Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - IBERAMIA '02
Evolutionary product-unit neural networks classifiers

Neurocomputing
A two-stage algorithm in evolutionary product unit neural networks for classification

Expert Systems with Applications: An International Journal

Evolutionary generation of neural network classifiers-An empirical comparison

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

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 developed by us; lastly, the reduction percentage in the number of inputs is above a fifty five, on average.