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http://dx.doi.org/10.5392/JKCA.2010.10.5.001

Ant Colony Optimization for Feature Selection in Pattern Recognition  

Oh, Il-Seok (전북대학교 컴퓨터공학부/영상정보신기술연구소)
Lee, Jin-Seon (우석대학교 게임콘텐츠학과)
Publication Information
The Journal of the Korea Contents Association / v.10, no.5, 2010 , pp. 1-9 More about this Journal
Abstract
This paper propose a novel scheme called selective evaluation to improve convergence of ACO (ant colony optimization) for feature selection. The scheme cutdown the computational load by excluding the evaluation of unnecessary or less promising candidate solutions. The scheme is realizable in ACO due to the valuable information, pheromone trail which helps identify those solutions. With the aim of checking applicability of algorithms according to problem size, we analyze the timing requirements of three popular feature selection algorithms, greedy algorithm, genetic algorithm, and ant colony optimization. For a rigorous timing analysis, we adopt the concept of atomic operation. Experimental results showed that the ACO with selective evaluation was promising both in timing requirement and recognition performance.
Keywords
Feature Selection; Greedy Algorithm; Genetic Algorithm; Ant Colony Optimization; Pattern Recognition;
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