A new uncertainty measure via belief Rényi entropy in Dempster-Shafer theory and its application to decision making

Zhe Liu, Yu Cao, Xiangli Yang and Lusi Liu

Communications in Statistics - Theory and Methods, 2024, vol. 53, issue 19, 6852-6868

Abstract: Dempster-Shafer theory (DST) has attracted wide attention in many fields thanks to its strong advantages over probability theory. Whereas the uncertainty measure of basic belief assignment (BBA) in DST is an open and essential problem. The main goal of this article is to propose a new belief Rényi entropy for the uncertainty measure of BBA, which is inspired by generalized Rényi entropy in DST. The proposed belief Rényi entropy satisfies some desirable properties of uncertainty measure. Furthermore, the proposed belief Rényi entropy can be degraded to Rényi entropy when BBA is transformed into a probability distribution. Finally, a new decision-making method is designed based on the proposed belief Rényi entropy. The validity of the proposed belief entropy is verified by some numerical examples and its application to decision-making.

Date: 2024
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DOI: 10.1080/03610926.2023.2253342

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