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Rough Set-Based Decision Tree Using A Core Attribute

Author

Listed:
  • 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)

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.

Suggested Citation

  • Sang-Wook Han & Jae-Yearn Kim, 2008. "Rough Set-Based Decision Tree Using A Core Attribute," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 7(02), pages 275-290.
    • Handle: RePEc:wsi:ijitdm:v:07:y:2008:i:02:n:s0219622008002946
    DOI: 10.1142/S0219622008002946
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