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A Multiple Classifier System based on Dynamic Classifier Selection having Local Property  

송혜정 (한림대학교 컴퓨터공학과)
김백섭 (한림대학교 정보통신공학부)
Publication Information
Journal of KIISE:Software and Applications / v.30, no.3_4, 2003 , pp. 339-346 More about this Journal
Abstract
This paper proposes a multiple classifier system having massive micro classifiers. The micro classifiers are trained by using a local set of training patterns. The k nearest neighboring training patterns of one training pattern comprise the local region for training a micro classifier. Each training pattern is incorporated with one or more micro classifiers. Two types of micro classifiers are adapted in this paper. SVM with linear kernel and SVM with RBF kernel. Classification is done by selecting the best micro classifier among the micro classifiers in vicinity of incoming test pattern. To measure the goodness of each micro classifier, the weighted sum of correctly classified training patterns in vicinity of the test pattern is used. Experiments have been done on Elena database. Results show that the proposed method gives better classification accuracy than any conventional classifiers like SVM, k-NN and the conventional classifier combination/selection scheme.
Keywords
Multiple Classifier System; Dynamic Classifier selection; local accuracy; SVM; local learning;
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