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)


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.


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.


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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

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