Induction of decision trees from partially classified data using belief functions

A new tree-structured classifier based on the Dempster-Shafer theory of evidence is presented. The entropy measure classically used to assess the impurity of nodes in decision trees is replaced by an evidence-theoretic uncertainty measure taking into account not only the class proportions, but also the number of objects in each node. The resulting algorithm allows the processing of training data whose class membership is only partially specified in the form of a belief function. Experimental results with EEG data are presented.

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