International Journal of Fuzzy Logic and Intelligent Systems

  • 제3권1호
  • /
  • Pages.44-51
  • /
  • 2003
  • /
  • 1598-2645(pISSN)
  • /
  • 2093-744X(eISSN)
한국지능시스템학회 (Korean Institute of Intelligent Systems)
권호 표지
DOI QR코드

DOI QR Code

Fuzzy Classification Rule Learning by Decision Tree Induction

  • Lee, Keon-Myung (School of Electric and Computer Engineering, Research Institute for Computer and Information Communication, Chungbuk National University and Advanced Information Technology Research Center(AITrc)) ;
  • Kim, Hak-Joon (Div. of Electrical, Electronic, Information, Communication Engineering, Howon University)
  • 발행 : 2003.06.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Knowledge acquisition is a bottleneck in knowledge-based system implementation. Decision tree induction is a useful machine learning approach for extracting classification knowledge from a set of training examples. Many real-world data contain fuzziness due to observation error, uncertainty, subjective judgement, and so on. To cope with this problem of real-world data, there have been some works on fuzzy classification rule learning. This paper makes a survey for the kinds of fuzzy classification rules. In addition, it presents a fuzzy classification rule learning method based on decision tree induction, and shows some experiment results for the method.

키워드

참고문헌

  1. T. M. Mitchell, Machine Leaming, The McGraw-HillCo., 414P, 1997
  2. Y. Yuan, M. J. Shaw, Induction of fuzzy decision trees, Fuzzy Sets and Systems, Vol.69, PP.125-139, 1995 https://doi.org/10.1016/0165-0114(94)00229-Z
  3. A. Ittner, J. Zeidler, R. Rossius, W. Dilger, M.Schlosser, feature space partitioning by non-linear andfuzzy decision trees, Proc. of the 7-th IFSA World Congress(Prague, Czech), PP.394-398, 1997
  4. J. Zeidler, M. Schlosser, Continuous-valued attributes infuzzy decision trees, Proc. of the 6-th Int. Conf. onInformation, Processing and Management of Uncertaintyin Knowledge-Based Systems (Granada, Spain),PP.395-400, 1996
  5. D. Rasmussen, R. R. Yager, SummarySQL: A fuzzy toolfor data mining, Intelligent Data Analysis, Vol.1, No.1,1997
  6. S. A. Maelainin, A. Bensaid, Fuzzy data mining querylanguage, Proc. of the Second Int. Conf. onKnowIedge-Based Systems(Adelaide,Australia),PP.335-340, 1998
  7. Y. El-Sonbaty, M. A. Ismail, Fuzzy Clustering for Symbolic Data, IEEE Trans. on Fuzzy Systems, Vol.6, No.2, pp.195-204, 1998 https://doi.org/10.1109/91.669013
  8. C. Z. Janikow, Fuzzy Decision Trees: Issues andMethods, IEEE Trans. on Systems, Man, and Cybernetics- Part B, Vol.28, No.1, PP. 1-14, 1998
  9. K-. Nozaki, H. Ishibuch, H. Tanaka, Adaptive FuzzyRule-based Classification Systems, IEEE Trans. on Fuzzy Systems, Vol.4, No.3, PP.238-250, 1996 https://doi.org/10.1109/91.531768
  10. R..L.P. Chang, T. Pavlidis, Fuzzy Decision TreeAlgorithms, IEEE Trans. on Systems, Man, and Cybernetics, Vol.7, No.1, pp.28-35, 1977 https://doi.org/10.1109/TSMC.1977.4309586
  11. T. Tani, M. Sakoda, Fuzzy modeling by ID3 algorithmand its application to prediction of heater outlettemperature, Proc. of IEEE Int. Conf. on Fuzzy Systems(San Diego), PP.923-930, 1992
  12. R. Weber, Fuzzy-ID3: a class of methods for automaticknowledge acquisition, Proc. of 2nd Int. Conf. onFuzzy Logic and Neural Networks(Iizuka), PP.265-268,1992
  13. J. Jang, Structure determination in fuzzy modeling: Afuzzy CART approach, Proc. IEEE Conf. on Fuzzy Systems, PP.480-485, 1994
  14. H.-J. Zimmermann, Fuzzy Set Theory and its Applications, Kluwer Academic Pub., 399P, 1991