한국지능시스템학회논문지 (Journal of the Korean Institute of Intelligent Systems)

  • 제11권7호
  • /
  • Pages.666-674
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  • 2001
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  • 1976-9172(pISSN)
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  • 2288-2324(eISSN)
한국지능시스템학회 (Korean Institute of Intelligent Systems)
권호 표지

Rough Set-based Incremental Inductive Learning Algorithm Theory and Applications

  • Bang, Won-Chul (Human-friendly Welfare Robot System Engineering Research Center, KAIST) ;
  • Z. Zenn Bien (Division of Electrical Engineering, Dept. of EECS, KAIST)
  • 발행 : 2001.12.01
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초록

Classical methods to find a minimal set of rules based on the rough set theory are known to be ineffective in dealing with new instances added to the universe. This paper introduces an inductive learning algorithm for incrementally retrieving a minimal set of rules from a given decision table. Then, the algorithm is validated via simulations with two sets of data, in comparison with a classical non-incremental algorithm. The simulation results show that the proposed algorithm is effective in dealing with new instances, especially in practical use.

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참고문헌

  1. Proc. Int. Symp. on Computation Theory and Lecture Notes in Computer Science v.208 On Learning A Rough Set Approach Z. Pawlak;G. Goos.(et al.)(eds.)
  2. International Journal of Fuzzy Systems v.1 no.1 New Incremental Inductive Learning Algorithm in the Framework of Rough Set Theory Won-Chul Bang and Zeungnam Bien
  3. Rough Sets:Theoretical Aspects and Reasoning about data Z. Pawlak
  4. Annals of Eugenics v.7 The use of multiple measurements in taxonomic problems R. A. Fisher
  5. Machine Learning: Principles and Techniques(Chapman and Hall Computing Series) R. Forsyth