Evolutionary-class independent LDA as a pre-process for improving classification

Authors:
Hossein Moeinzadeh;M-Mehdi Mohammadi;Ahmad Akbari;Babak Nasersharif
Affiliations:
Research center of Information technology, Department of Computer Engineering Iran University of Science and Technology , Tehran, Iran;Research center of Information technology, Department of Computer Engineering , Iran University of Science and Technology , Tehran, Iran;Research center of Information technology, Department of Computer Engineering ,Iran University of Science and Technology , Tehran, Iran;Department of Computer Engineering, Faculty of Engineering University of Guilan, Rasht, Iran
Venue:
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Year:
2009

Citing 2
Cited 1

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LDA pre-processing for classification: class-dependent single objective GA and multi-objective GA approaches

IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning

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Abstract

An appropriate pre-processing algorithm in classification is important and crucial with respect to classifier type. In this paper, two pre-processing methods are suggested to be applied before classification in order to increase classification accuracy. The aim of this approach is finding a transformation matrix to discriminate between classes by transforming data into the new space. In the first method, we use class independent LDA to increase classification accuracy. Because LDA cannot obtain optimal transformation, in the second approach, two evolutionary methods (Genetic Algorithm and Particle Swarm Optimization) are used to increase performance of LDA. 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.