Open Science Research Excellence
%0 Journal Article
%A Salsabil Trabelsi and 
Zied Elouedi and 
Khaled Mellouli
%D 2008 
%J  International Journal of Computer, Electrical, Automation, Control and Information Engineering
%B World Academy of Science, Engineering and Technology
%I International Science Index 21, 2008
%T Pruning Method of Belief Decision Trees
%U http://waset.org/publications/3379
%V 21
%X The belief decision tree (BDT) approach is a decision
tree in an uncertain environment where the uncertainty is represented
through the Transferable Belief Model (TBM), one interpretation
of the belief function theory. The uncertainty can appear either in
the actual class of training objects or attribute values of objects to
classify. In this paper, we develop a post-pruning method of belief
decision trees in order to reduce size and improve classification
accuracy on unseen cases. The pruning of decision tree has a
considerable intention in the areas of machine learning.
%P 3207 - 3212