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Basic probability assignment to probability distribution function based on the Shapley value approach
Author(s) -
Huang Chongru,
Mi Xiangjun,
Kang Bingyi
Publication year - 2021
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.22456
Subject(s) - transformation (genetics) , probabilistic logic , computer science , probability distribution , mathematical optimization , set (abstract data type) , shapley value , function (biology) , characteristic function (probability theory) , element (criminal law) , algorithm , probability density function , mathematics , artificial intelligence , statistics , mathematical economics , game theory , biochemistry , chemistry , evolutionary biology , biology , political science , law , gene , programming language
In Dempster–Shafer evidence theory, how to use the basic probability assignment (BPA) in decision‐making is a significant issue. The transformation of BPA into a probability distribution function is one of the common and feasible schemes. To overcome the problems of the existing methods, we propose a marginal probability transformation method based on the Shapley value approach. The proposed method allocates BPA values in terms of how much an element contributes to a set, which is an equitable and effective distribution mechanism. Furthermore, we use probabilistic information content to evaluate the effect of each transformation method. Moreover, some numerical examples are used to demonstrate the efficiency and feasibility of the proposed method. Further, two applications, target recognition, fault diagnosis are used to verify the superiority and effectiveness of the proposed method in practice.