Proceedings of the Korean Institute of Intelligent Systems Conference (한국지능시스템학회:학술대회논문집)

Korean Institute of Intelligent Systems (한국지능시스템학회)
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Improvement of ID3 Using Rough Sets

라프셋 이론이 적용에 의한 ID3의 개선

  • Chung, Hong (Dept. of Computer and Electronic Engineering, Keimyung University) ;
  • Kim, Du-Wan (Dept. of Electronic and information Engineering, Catholic Univ. of Taegu Hyosung) ;
  • Chung, Hwan-Mook (Dept. of Electronic and information Engineering, Catholic Univ. of Taegu Hyosung)
  • 정홍 (계명대학교 컴퓨터 전자공학부) ;
  • 김두완 (대구효성가톨릭대학교 전자정보공학부) ;
  • 정환묵 (대구효성가톨릭대학교 전자정보공학부)
  • Published : 1997.10.01
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Abstract

This paper studies a method for making more efficient classification rules in the ID3 using the rough set theory. Decision tree technique of the ID3 always uses all the attributes in a table of examples for making a new decision tree, but rough set technique can in advance eleminate dispensable attributes. And the former generates only one type of classification rules, but the latter generates all the possibles types of them. The rules generated by the rough set technique are the simplist from as proved by the rough set theory. Therefore, ID3, applying the rough set technique, can reduct the size of the table of examples, generate the simplist form of the classification rules, and also implement an effectie classification system.

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