A Novel Optimization-Based Combination Rule for Dempster-Shafer Theory

被引：0

作者：

Turhan, Hasan Ihsan ^{[1
]}

Tanaydin, Tugba ^{[1
]}

机构：

[1] ASELSAN Inc, Ankara, Turkiye

来源：

BELIEF FUNCTIONS: THEORY AND APPLICATIONS, BELIEF 2024 | 2024年 / 14909卷

关键词：

Dempster-Shafer theory; Belief functions; Combination rule; Decision making; Optimization; BELIEF FUNCTIONS;

D O I：

10.1007/978-3-031-67977-3_19

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this study, a new methodology for combining probability masses from different sources is proposed for Dempster-Shafer theory. Unlike the existing works in the literature, this methodology treats the combination problem as an optimization problem and proposes an objective function that uses conflict and entropy measures to solve this problem. The proposed objective function aims to minimize the conflict and also to maximize the entropy of the combined probability masses. Thus, the difference between the combined probability masses and the masses coming from the sources is minimized while being cautious and avoiding a final certain decision. This newmethodology is tested in theMatlab environment and compared with the existing methods.

引用

页码：180 / 188

页数：9

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