Improving classification accuracy using evolutionary fuzzy transformation

Authors:
Hossein Moeinzadeh;Babak Nasersharif;Abdolazim Rezaee;Hossein Pazhoumand-dar
Affiliations:
Department of Computer Engineering, Iran University of Science and Technology, Tehran, Iran, Tehran, Iran;Department of Computer Engineering, Faculty of Engineering University of Guilan, Rasht, Iran, Guilan, Iran;Department of Computer Engineering, Islamic Azad University-Baft branch, Baft, Iran, Baft, Iran;Department of Computer Engineering, Islamic Azad University-Mashhad branch, Mashhad, Iran, Mashad, Iran
Venue:
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Year:
2009

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Abstract

Selection of a classifier is only one aspect of the problem of data classification. Equally important (if not, more so) is the pre-processing strategy to be employed. In this paper, a pre-processing step is proposed in order to increase accuracy of classification. The aim of this approach is finding a transformation matrix to discriminate between classes by transforming data into a new space. Obviously, this tends to increase the classification accuracy. This transformation matrix is computed through two evolutionary methods (GA and PSO) using fuzzy approach with the aim of increasing membership degree of data to their classes by transforming them into a new space. The transformation matrix is independent of classifier and classifier type has no effect on computation of transformation matrix. Obtained results show that these pre-processing methods increase the accuracy of different classifiers.