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Classifier Selection using Feature Space Attributes in Local Region  

Shin Dong-Kuk (메디슨 DS팀)
Song Hye-Jeong (한림대학교 정보통신공학부)
Kim Baeksop (한림대학교 정보통신공학부)
Publication Information
Journal of KIISE:Software and Applications / v.31, no.12, 2004 , pp. 1684-1690 More about this Journal
Abstract
This paper presents a method for classifier selection that uses distribution information of the training samples in a small region surrounding a sample. The conventional DCS-LA(Dynamic Classifier Selection - Local Accuracy) selects a classifier dynamically by comparing the local accuracy of each classifier at the test time, which inevitably requires long classification time. On the other hand, in the proposed approach, the best classifier in a local region is stored in the FSA(Feature Space Attribute) table during the training time, and the test is done by just referring to the table. Therefore, this approach enables fast classification because classification is not needed during test. Two feature space attributes are used entropy and density of k training samples around each sample. Each sample in the feature space is mapped into a point in the attribute space made by two attributes. The attribute space is divided into regular rectangular cells in which the local accuracy of each classifier is appended. The cells with associated local accuracy comprise the FSA table. During test, when a test sample is applied, the cell to which the test sample belongs is determined first by calculating the two attributes, and then, the most accurate classifier is chosen from the FSA table. To show the effectiveness of the proposed algorithm, it is compared with the conventional DCS -LA using the Elena database. The experiments show that the accuracy of the proposed algorithm is almost same as DCS-LA, but the classification time is about four times faster than that.
Keywords
Classifier Combination; Multiple Classifier System; Dynamic Classifier selection; local accuracy; local learning;
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