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Induction of decision trees from partially classified data using belief functions

37 citations0/0 files storedConference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics

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Multi-Criteria Decision Making Data Mining Algorithms and Applications Rough Sets and Fuzzy Logic

Abstract

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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References

1995A k-nearest neighbor classification rule based on Dempster-Shafer theory 2008A k-Nearest Neighbor Classification Rule Based on Dempster-Shafer Theory 1998Uncertainty-Based Information: Elements of Generalized Information Theory 2000A neural network classifier based on Dempster-Shafer theory 1998An evidence-theoretic k-NN rule with parameter optimization 1997Analysis of evidence-theoretic decision rules for pattern classification

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2004On aggregating belief decision trees

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