Uncertainty measure based on Tsallis entropy in evidence theory

被引：67

作者：

Gao, Xiaozhuan ^{[1
]}

Liu, Fan ^{[1
]}

Pan, Lipeng ^{[1
]}

Deng, Yong ^{[1
]}

Tsai, Sang-Bing ^{[2
]}

机构：

[1] Univ Elect Sci & Technol China, Inst Fundamental & Frontier Sci, Chengdu 610054, Sichuan, Peoples R China

[2] Univ Elect Sci & Technol China, Zhongshan Inst, Zhongshan 528402, Guangdong, Peoples R China

来源：

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS | 2019年 / 34卷 / 11期

基金：

中国国家自然科学基金;

关键词：

basic probability assignment; belief function; Dempster-Shafer evidence theory; Tsallis entropy; uncertainty; COMBINING BELIEF FUNCTIONS; DEMPSTER-SHAFER THEORY; DECISION-MAKING METHOD; DIVERGENCE MEASURE; DENG ENTROPY; D NUMBERS; CLASSIFICATION; SPECIFICITY; COMBINATION; FRAMEWORK;

D O I：

10.1002/int.22185

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：3105 / 3120

页数：16

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