RSGALS-SVM: random subspace method applied to a LS-SVM ensemble optimized by genetic algorithm

Authors:
Carlos Padilha;Adrião D. Dória Neto;Jorge D. Melo
Affiliations:
Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte, Natal, Brazil;Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte, Natal, Brazil;Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte, Natal, Brazil
Venue:
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
Year:
2012

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Abstract

The Support Vector Machines (SVMs) have received great emphasis in the pattern classification due its good ability to generalize. The Least Squares formulation of SVM (LS-SVM) finds the solution by solving a set of linear equations instead of quadratic programming. Both the SVMs and the LS-SVMs provide some free parameters that have to be tuned to reflect the requirements of the given task. Despite their high performance, lots of tools have been developed to improve them, mainly the development of new classifying methods and the employment of ensembles. So, in this paper, our proposal is to use both the theory of ensembles and a genetic algorithm to enhance the LS-SVM classification. First, we randomly divide the problem into subspaces to generate diversity among the classifiers of the ensemble. So, we apply a genetic algorithm to optimize the classification of this ensemble of LS-SVM, testing with some benchmark data sets.