ROUGH SET-BASED DECISION TREE USING A CORE ATTRIBUTE

Sang-Wook Han () and Jae-Yearn Kim
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Sang-Wook Han: Department of Industrial Engineering, Hanyang University, 17 Haengdang-dong, Seongdong-ku, Seoul 133-791, Republic of Korea
Jae-Yearn Kim: Department of Industrial Engineering, Hanyang University, 17 Haengdang-dong, Seongdong-ku, Seoul 133-791, Republic of Korea

International Journal of Information Technology & Decision Making (IJITDM), 2008, vol. 07, issue 02, 275-290

Abstract: Decision trees are widely used in machine learning and artificial intelligence. In this paper, we extend previous research and present a new decision tree classification algorithm that uses a rough set theory to produce classification rules. Our algorithm is based on core attributes and on comparing the values of attributes between objects. Our experiments compared the performance of the Iterative Dichotomiser 3 (ID3) algorithm, C4.5, and the proposed decision tree algorithm to demonstrate its accuracy and ability to simplify rules.

Keywords: Core; reduct; decision tree; discernibility matrix; rough set (search for similar items in EconPapers)
Date: 2008
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DOI: 10.1142/S0219622008002946

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