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Complexity
Volume 2017, Article ID 4359195, 15 pages
https://doi.org/10.1155/2017/4359195
Research Article

An Improved Belief Entropy and Its Application in Decision-Making

School of Electronics and Information, Northwestern Polytechnical University, Xi’an, Shaanxi 710072, China

Correspondence should be addressed to Yongchuan Tang; nc.ude.upwn.liam@nauhcgnoygnat and Wen Jiang; nc.ude.upwn@newgnaij

Received 22 December 2016; Accepted 23 January 2017; Published 16 March 2017

Academic Editor: Jurgita Antucheviciene

Copyright © 2017 Deyun Zhou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Uncertainty measure in data fusion applications is a hot topic; quite a few methods have been proposed to measure the degree of uncertainty in Dempster-Shafer framework. However, the existing methods pay little attention to the scale of the frame of discernment (FOD), which means a loss of information. Due to this reason, the existing methods cannot measure the difference of uncertain degree among different FODs. In this paper, an improved belief entropy is proposed in Dempster-Shafer framework. The proposed belief entropy takes into consideration more available information in the body of evidence (BOE), including the uncertain information modeled by the mass function, the cardinality of the proposition, and the scale of the FOD. The improved belief entropy is a new method for uncertainty measure in Dempster-Shafer framework. Based on the new belief entropy, a decision-making approach is designed. The validity of the new belief entropy is verified according to some numerical examples and the proposed decision-making approach.