A hybrid evolutionary approach to obtain better quality classifiers

Authors:
David Becerra-Alonso;Mariano Carbonero-Ruz;Francisco José Martínez-Estudillo;Alfonso Carlos Martínez-Estudillo
Affiliations:
Department of Management and Quantitative Methods, ETEA - University of Córdoba;Department of Management and Quantitative Methods, ETEA - University of Córdoba;Department of Management and Quantitative Methods, ETEA - University of Córdoba;Department of Management and Quantitative Methods, ETEA - University of Córdoba
Venue:
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
Year:
2011

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Abstract

We present an extra measurement for classifiers, responding to the need to evaluate them with more than accuracy alone. This measure should be able to express, at least to some degree, the extent to which all classes are taken into account in a classification problem. In this communication we propose sensitivity dispersion (being as it is, the associated statistical dispersion measurement of accuracy), as the appropriate measure to have a more complete evaluation of the quality of classifiers. We use the Evolutionary Extreme Learning Machine algorithm, with a specific fitness function to optimize both measures simultaneously, and we compare it with other classifiers.