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http://dx.doi.org/10.5392/JKCA.2011.11.10.041

Convergence Characteristics of Ant Colony Optimization with Selective Evaluation in Feature Selection  

Lee, Jin-Seon (우석대학교 게임콘텐츠학과)
Oh, Il-Seok (전북대학교 컴퓨터공학부/영상정보신기술연구소)
Publication Information
The Journal of the Korea Contents Association / v.11, no.10, 2011 , pp. 41-48 More about this Journal
Abstract
In feature selection, the selective evaluation scheme for Ant Colony Optimization(ACO) has recently been proposed, which reduces computational load by excluding unnecessary or less promising candidate solutions from the actual evaluation. Its superiority was supported by experimental results. However the experiment seems to be not statistically sufficient since it used only one dataset. The aim of this paper is to analyze convergence characteristics of the selective evaluation scheme and to make the conclusion more convincing. We chose three datasets related to handwriting, medical, and speech domains from UCI repository whose feature set size ranges from 256 to 617. For each of them, we executed 12 independent runs in order to obtain statistically stable data. Each run was given 72 hours to observe the long-time convergence. Based on analysis of experimental data, we describe a reason for the superiority and where the scheme can be applied.
Keywords
Pattern Recognition; Feature Selection; Convergence; Meta-heuristics; Selective Evaluation;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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