Partical Swarm Optimization

ARTICLE IN PRESS

European Journal of Operational Research xxx (2010) xxx–xxx

Contents lists available at ScienceDirect

European Journal of Operational Research

journal homepage: www.elsevier.com/locate/ejor

Discrete Optimization

A discrete particle swarm optimization method for feature selection in binary classification problems

Alper Unler *, Alper Murat

Department of Industrial and Manufacturing Engineering, Wayne State University, 4815 Fourth St. Detroit, MI 48202, USA

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This paper investigates the feature subset selection problem for the binary classification problem using logistic regression model. We developed a modified discrete particle swarm optimization (PSO) algorithm for the feature subset selection problem. This approach embodies an adaptive feature selection procedure which dynamically accounts for the relevance and dependence of the features included the feature subset. We compare the proposed methodology with the tabu search and scatter search algorithms using publicly available datasets. The results show that the proposed discrete PSO algorithm is competitive in terms of both classification accuracy and computational performance. Ó 2010 Elsevier B.V. All rights reserved.

Article history: Received 29 April 2009 Accepted 23 February 2010 Available online xxxx Keywords: Feature selection Particle swarm optimization Metaheuristics Binary classification Logistic regression

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