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Pruning Method of Belief Decision Trees

By Salsabil Trabelsi, Zied Elouedi and Khaled Mellouli

Abstract

Abstract — 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

Topics: machine learning, uncertainty, belief function theory
Year: 2011
OAI identifier: oai:CiteSeerX.psu:10.1.1.193.4987
Provided by: CiteSeerX
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