Uncertainty measure based on Tsallis entropy in evidence theory. Issue 11 (18th September 2019)

• Record Type: Journal Article
• Authors: Gao, Xiaozhuan; Liu, Fan; Pan, Lipeng; Deng, Yong; Tsai, Sang‐Bing
• Is Part Of: International journal of intelligent systems. Volume 34:Issue 11(2019)
• Page Start: 3105
• Page End: 3120
• Publication Date: 2019-09-18
• Subjects: basic probability assignment -- belief function -- Dempster‐Shafer evidence theory -- Tsallis entropy -- uncertainty; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; 006.3
• DOI: 10.1002/int.22185
• Languages: English
• ISSNs: 0884-8173

Abstract

Abstract: Dempster‐Shafer evidence theory has been widely used in many applications due to its advantages with weaker conditions than Bayes probability. How to measure the uncertainty of basic probability assignment (BPA) in Dempster‐Shafer evidence theory is an open and essential issue. Tsallis entropy as nonextensive entropy proposed according to multifractals has been used in many fields. In this paper, a new uncertainty measure of BPA is presented based on Tsallis entropy. The key issue is to determine the value of q in Tsallis entropy. In addition, this paper also analyzes the properties of proposed uncertainty measure. Some numerical examples are used to illustrate the efficiency of the proposed method. Finally, the paper also discusses the application of the proposed method in decision‐making.