EDTs: evidential decision trees

Authors:
Huawei Guo;Wenkang Shi;Feng Du
Affiliations:
School of Electronics and Information Technology, Shanghai Jiao Tong University, Shanghai, P.R. China;School of Electronics and Information Technology, Shanghai Jiao Tong University, Shanghai, P.R. China;School of Electronics and Information Technology, Shanghai Jiao Tong University, Shanghai, P.R. China
Venue:
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
Year:
2005

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Abstract

In uncertain environment, this paper investigates the induction of decision trees based on D-S evidence theory. This framework allows us to handle the case where the test attributes and decision attribute of training instances are all represented by belief functions. A novel attribute selection measure is introduced. We also propose a new evidential combination rule to combine the classification results with different matching coefficients.