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의사결정나무의 광역 최적화를 이용한 분류 방법

Title
의사결정나무의 광역 최적화를 이용한 분류 방법
Authors
조윤주
Date Issued
2012
Publisher
포항공과대학교
Abstract
Classification is to generate a set of rule of classifying objects into several categories based on the training sample. Decision tree as a classification tool is being used successfully, because it has the advantage of being a knowledge representation intuitively comprehensible to the user. In this paper, we propose a new classification using optimization of decision tree. The proposed method consists of three phases. First, we choose the relevant variables using a well-known decision tree algorithm, classification and regression tree(CART). Second, we find the optimum thresholds simultaneously using adaptive particle swarm optimization(APSO) for those selected variables. Third, we simplify the set of IF-THEN rules. Additionally, we repeat the procedure to find more improved rules by changing the splitting variables. To validate the proposed method, several artificial and real datasets are used. We compare our results with the original CART results and show that the proposed algorithm is promising for improving prediction accuracy.
URI
http://postech.dcollection.net/jsp/common/DcLoOrgPer.jsp?sItemId=000001218028
http://oasis.postech.ac.kr/handle/2014.oak/1448
Article Type
Thesis
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