Computability in Europe 2008
Logic and Theory of Algorithms

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The Use of Information Affinity in Possibilistic Decision Tree Learning and Evaluation

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Author(s): Ilyes Jenhani, Zied Elouedi and Salem Benferhat


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
criterion which we have used in order to show the performance of
the approach on well-known benchmarks.

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