Learning and Evaluating Possibilistic Decision Trees using Information Affinity

  title={Learning and Evaluating Possibilistic Decision Trees using Information Affinity},
  author={Ilyes Jenhani and Salem Benferhat and Zied Elouedi},
This paper investigates the issue of building decision trees from data with imprecise class values where imprecision is encoded in the form of possibility distributions. The Information Affinity similarity measure is introduced into the well-known gain ratio criterion in order to assess the homogeneity of a set of possibility distributions representing instances’s classes belonging to a given training partition. For the experimental study, we proposed an information affinity based performance… CONTINUE READING
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