mr2PSO: A maximum relevance minimum redundancy feature selection method based on swarm intelligence for support vector machine classification

  • Authors:

  • Alper Unler;Alper Murat;Ratna Babu Chinnam

  • Affiliations:

  • Department of Information Management Systems, KKK, Yucetepe, Ankara, Turkey;Department of Industrial and Manufacturing Engineering, Wayne State University, Detroit, MI, USA;Department of Industrial and Manufacturing Engineering, Wayne State University, Detroit, MI, USA

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2011

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Abstract

This paper presents a hybrid filter-wrapper feature subset selection algorithm based on particle swarm optimization (PSO) for support vector machine (SVM) classification. The filter model is based on the mutual information and is a composite measure of feature relevance and redundancy with respect to the feature subset selected. The wrapper model is a modified discrete PSO algorithm. This hybrid algorithm, called maximum relevance minimum redundancy PSO (mr^2PSO), is novel in the sense that it uses the mutual information available from the filter model to weigh the bit selection probabilities in the discrete PSO. Hence, mr^2PSO uniquely brings together the efficiency of filters and the greater accuracy of wrappers. The proposed algorithm is tested over several well-known benchmarking datasets. The performance of the proposed algorithm is also compared with a recent hybrid filter-wrapper algorithm based on a genetic algorithm and a wrapper algorithm based on PSO. The results show that the mr^2PSO algorithm is competitive in terms of both classification accuracy and computational performance.