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학술지 Identifying Core Sets of Discriminatory Features using Particle Swarm Optimization
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저자
W. Pedrycz, 박병준, N.J. Pizzi
발행일
200904
출처
Expert Systems with Applications, v.36 no.3, pp.4610-4616
ISSN
0957-4174
출판사
Elsevier
DOI
https://dx.doi.org/10.1016/j.eswa.2008.05.017
협약과제
08MC3600, Pro-active Idle-Stop을 위한 가상센서기반 Situation-Aware 기술개발, 손명희
초록
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. © 2008 Elsevier Ltd. All rights reserved.
키워드
Classification, Computational intelligence, Feature selection, Particle swarm optimization
KSP 제안 키워드
Analysis of results, Classification problems, Comparative analysis, Computational intelligence, Feature selection(FS), Feature set, Feature space, Nearest Neighbor Classifier, Numerical experiments, Optimization techniques(OT), Pattern recognition