A Soft Discretization Technique for Fuzzy Decision Trees Using Resampling

  • Taimur Qureshi
  • D. A. Zighed
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5788)

Abstract

Decision trees generate classifiers from training data through a process of recursively splitting the data space. In the case of training on continuous-valued data, the associated attributes must be discretized into several intervals using a set of crisp cut points. One drawback of decision trees is their instability, i.e., small data deviations may require a significant reconstruction of the decision tree. Here, we present a novel soft decision tree method that uses soft of fuzzy discretization instead of traditional crisp cuts. We use a resampling based technique to generate soft discretization points and demonstrate the advantages of using our resampling based soft discretization over traditional crisp methods.

Keywords

Crisp discretization resampling fuzzy discretization 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Quinlan, J.R.: Decision Trees and Decision Making. IEEE Transactions on System, Man and Cybernetic, 339–346 (1990)Google Scholar
  2. 2.
    Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Wadsworth International, San Francisco (1984)MATHGoogle Scholar
  3. 3.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. M. Kaufmann, SanMateo (1993)Google Scholar
  4. 4.
    Kerber, R.: Discretization of Numeric Attributes. In: Proceedings of the Tenth National Conference on Artificial Intelligence, pp. 123–128. MIT Press, Cambridge (1990)Google Scholar
  5. 5.
    Fayyad, U.M., Irani, K.: Multi-interval Discretization of Continuous-Valued Attributes for Classification Learning. In: Proceedings of the 13th International Joint Conference on Artificial Intelligence, pp. 1022–1027. Morgan Kaufmann, San Mateo (1993)Google Scholar
  6. 6.
    Efron, B., Tibshirani, R.: An Introduction to the Bootstrap. Chapman and Hall, Boca Raton (1998)MATHGoogle Scholar
  7. 7.
    Blake, C.L., Merz, C.J.: UCI Repository of machine learning databases. Irvine, CA: University of California, Department of Information and Computer Science (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
  8. 8.
    Zadeh, L.A.: Fuzzy Sets as a bases for a Theory of Possibility. Fuzzy Sets and Systems, 3–28 (1978)Google Scholar
  9. 9.
    Olaru, C., Wehenkel, L.: A complete fuzzy decision tree technique. Fuzzy Sets and Systems 138, 221–254 (2003)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Ramdani, M.: System d’Induction Formelle a base de Connaissanses Impresises. These de l’universite P et M Curie, rapport 94/1, LAFORIA IBP (1994)Google Scholar
  11. 11.
    Wang, T., Zhoujun, L., Yuejin, Y., Huowang, C.: A Survey of Fuzzy Decision Tree Classifier Methodology. In: ICFIE, pp. 959–968 (2007)Google Scholar
  12. 12.
    Shannon, C.E., Weaver, W.: The mathematical Theory of Communication. University of Illinois Press, Urbana (1949)MATHGoogle Scholar
  13. 13.
    Janikow, C.Z.: Fuzzy decision trees: issues and methods. IEEE Transactions on Systems, Man, and Cybernetics, Part B 28(1), 1–14 (1998)CrossRefGoogle Scholar
  14. 14.
    Ichihashi, H., Shirai, T., Nagasaka, K., Miyoshi, T.: Neuro fuzzy ID3: A method of inducing fuzzy decision trees with linear programming for maximizing entropy and algebraic methods. Fuzzy Sets System 81(1), 157–167 (1996)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Xizhao, W., Hong, J.: On the handling of fuzziness for continuous valued attributes in decision tree generation. Fuzzy Sets System 99, 283–290 (1998)MathSciNetCrossRefMATHGoogle Scholar
  16. 16.
    Murthy, S.K., Kasif, S., Salzberg, S.: A System for Induction of Oblique Decision Trees. Journal of AI Research (1994)Google Scholar
  17. 17.
    Marsala, C.: Application of Fuzzy Rule Induction to Data Mining. In: Andreasen, T., Christiansen, H., Larsen, H.L. (eds.) FQAS 1998. LNCS (LNAI), vol. 1495, pp. 260–271. Springer, Heidelberg (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Taimur Qureshi
    • 1
  • D. A. Zighed
    • 1
  1. 1.Laboratory ERICUniversity of Lyon 2Bron CedexFrance

Personalised recommendations