Advertisement

EDTs: Evidential Decision Trees

  • Huawei Guo
  • Wenkang Shi
  • Feng Du
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3614)

Abstract

In uncertain environment, this paper investigates the induction of decision trees based on D-S evidence theory. This framework allows us to handle the case where the test attributes and decision attribute of training instances are all represented by belief functions. A novel attribute selection measure is introduced. We also propose a new evidential combination rule to combine the classification results with different matching coefficients.

Keywords

Test Attribute Decision Attribute Combination Rule Belief Function Evidence Theory 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Zhou, Z.-H., Jiang, Y.: NeC4.5: neural ensemble based C4.5. IEEE Trans. Knowledge and Data Engineering 16, 770–773 (2004)CrossRefGoogle Scholar
  2. 2.
    Hern′andez, E., Recasens, J.: A general framework for induction of decision trees under uncertainty. In: Lawry, J., G. Shanahan, J., L. Ralescu, A. (eds.) Modelling with Words. LNCS, vol. 2873, pp. 26–43. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  3. 3.
    Quinlan, J.R.: Decision trees as probabilistic classifier. In: Proceedings of the Fourth international Machine Learning, pp. 31–37 (1987)Google Scholar
  4. 4.
    Yuan, Y., Shaw, M.J.: Induction of fuzzy decision trees. Fuzzy sets Syst. 69, 125–139 (1995)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Elouedi, Z., Mellouli, K., Smets, P.: Belief Decision Trees: Theoretical Foundations. Int. J. Approx. Reas. 28, 91–124 (2001)zbMATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Denoeux, T., Bjanger, M.: Induction of decision trees from partially classified data using belief functions. In: Proceedings of SMC 2000, Nashville, USA, pp. 2923–2928 (2000)Google Scholar
  7. 7.
    Dempster, A.P.: Upper and lower probabilities induced by a multi-valued mapping. Annals of Math. Stat. 38, 325–339 (1967)zbMATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Shafer, G.: A Mathematical Theory of Evidence. Princeton Univ. Press, Princeton (1976)zbMATHGoogle Scholar
  9. 9.
    Smets, P., Kennes, R.: The transferable belief model. Artificial Intelligence 66, 191–243 (1994)zbMATHCrossRefMathSciNetGoogle Scholar
  10. 10.
    Delmotte, F., Smets, P.: Target identification based on the transferable belief model interpretation of Dempster–Shafer model. IEEE Trans. Syst., Man, Cybern. 34, 457–470 (2004)CrossRefGoogle Scholar
  11. 11.
    Deng, Y., Shi, W.K., Zhu, Z.F.: Efficient combination approach of conflict evidence. J. Infrared Millim. W. 23(1), 27–32 (2004)Google Scholar
  12. 12.
    Sammon Jr., J.W.: A nonlinear mapping for data structure analysis. IEEE Trans. Computers C-18, 401–409 (1969)CrossRefGoogle Scholar
  13. 13.
    Denoeux, T., Masson, M.: EVCLUS: Evidential clustering of proximity data. IEEE Trans. Syst., Man, Cybern., B 34, 95–109 (2004)CrossRefGoogle Scholar
  14. 14.
    Anne-Laure, J., Dominic, G., Éloi, B.: A new distance between two bodies of evidence. Inform. Fusion 2, 91–101 (2001)CrossRefGoogle Scholar
  15. 15.
    Wang, X.Z., Zhao, M.H., Wang, D.H.: Selection of parameters in building fuzzy decision trees. In: Proceedings of 16th Australian Conference on AI, Perth, Australia, pp. 282–292 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Huawei Guo
    • 1
  • Wenkang Shi
    • 1
  • Feng Du
    • 1
  1. 1.School of Electronics and Information TechnologyShanghai Jiao Tong UniversityShanghaiP.R. China

Personalised recommendations