English, Article edition: ROUGH SET-BASED DECISION TREE USING A CORE ATTRIBUTE SANG-WOOK HAN; JAE-YEARN KIM

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Physical Description
  • article
Language
  • English

Edition details

Title
  • ROUGH SET-BASED DECISION TREE USING A CORE ATTRIBUTE
Author
  • SANG-WOOK HAN
  • JAE-YEARN KIM
Physical Description
  • article
Notes
  • 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.
  • Core, reduct, decision tree, discernibility matrix, rough set
  • RePEc:wsi:ijitdm:v:07:y:2008:i:02:p:275-290
Language
  • English
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