JACIII Vol.15 No.6 pp. 671-680
doi: 10.20965/jaciii.2011.p0671


Ant Colony Optimization for Feature Selection Involving Effective Local Search

Md. Monirul Kabir*, Md. Shahjahan**, and Kazuyuki Murase*,***

*Department of System Design Engineering, Graduate School of Engineering, University of Fukui, 3-9-1 Bunkyo, Fukui 910-8507, Japan

**Department of Electrical and Electronic Engineering, Khulna University of Engineering and Technology, Building no-13E, KUET Campus, Khulna 9203, Bangladesh

***Research and Education Program for Life Science, University of Fukui, Japan

December 20, 2010
April 27, 2011
August 20, 2011
feature selection, local search, ant colony optimization algorithm, neural network
This paper proposes an effective algorithm for feature selection (ACOFS) that uses a global Ant Colony Optimization algorithm (ACO) search strategy. To make ACO effective in feature selection, our proposed algorithm uses an effective local search in selecting significant features. The novelty of ACOFS lies in its effective balance between ant exploration and exploitation using new pheromone update and heuristic information computation rules to generate a subset of a smaller number of significant features. We evaluate algorithm performance using seven real-world benchmark classification datasets. Results show that ACOFS generates smaller subsets of significant features with improved classification accuracy.
Cite this article as:
M. Kabir, M. Shahjahan, and K. Murase, “Ant Colony Optimization for Feature Selection Involving Effective Local Search,” J. Adv. Comput. Intell. Intell. Inform., Vol.15 No.6, pp. 671-680, 2011.
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