Identifying core sets of discriminatory features using particle swarm optimization

  • Authors:

  • W. Pedrycz;B. J. Park;N. J. Pizzi

  • Affiliations:

  • Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada T6R 2G7 and Systems Research Institute, Polish Academy of Sciences, PL-01-447 Warsaw, Poland;Telematics & USN Research Department, Electronics and Telecommunications Research Institute (ETRI), Daejeon, 305-700, South Korea;Institute for Biodiagnostics National Research Council, Winnipeg, MB, Canada R3B 1Y6 and Department of Computer Science University of Manitoba, Winnipeg, MB, Canada R3T 2N2

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2009

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Abstract

Forming an efficient feature space for classification problems is a grand challenge in pattern recognition. New optimization techniques emerging in areas such as Computational Intelligence have been investigated in the context of feature selection. Here, we propose an original two-phase feature selection method that uses particle swarm optimization (PSO), a biologically inspired optimization technique, which forms an initial core set of discriminatory features from the original feature space. This core set is then successively expanded by searching for additional discriminatory features. The performance of the proposed PSO feature selection method is evaluated using a nearest neighbor classifier. The design of the optimally reduced feature space is investigated in a parametric setting by varying the size of the core feature set and the training set. Numerical experiments, using data from the Machine Learning Repository, show that a substantial reduction of the feature space is accomplished. A thorough comparative analysis of results reported in the literature also reveals improvement in classification accuracy.