%0 Journal Article
	%A Ilyes Jenhani and  Salem Benferhat and  Zied Elouedi
	%D 2010
	%J International Journal of Computer and Information Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 39, 2010
	%T Learning and Evaluating Possibilistic Decision Trees using Information Affinity
	%U https://publications.waset.org/pdf/5039
	%V 39
	%X 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
	%P 473 - 479