{"title":"A Belief Similarity Measure for Dempster-Shafer Evidence Theory and Application in Decision Making","authors":"Zhe Liu","doi":"10.31181/jscda21202443","DOIUrl":null,"url":null,"abstract":"How to effectively deal with uncertain and imprecise information in decision making is a complex task. Dempster-Shafer evidence theory (DSET) is widely used for handling such challenges due to its ability to model uncertainty and imprecision. However, Dempster's rule can sometimes yield counterintuitive results when dealing with highly conflicting evidence. In this paper, we introduce a novel belief sine similarity measure, called $BS^2M$, which effectively measures the discrepancy between different pieces of evidence. We also establish that $BS^2M$ possesses important properties such as boundedness, symmetry, and non-degeneracy. Building upon $BS^2M$, we present a new method for decision making. The proposed method considers both the credibility and the information volume of each evidence, providing a more comprehensive reflection of their importance. To validate our method, we conduct experiment in target recognition application, demonstrating the effectiveness and rationality of the proposed method.","PeriodicalId":313803,"journal":{"name":"Journal of Soft Computing and Decision Analytics","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Soft Computing and Decision Analytics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.31181/jscda21202443","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
How to effectively deal with uncertain and imprecise information in decision making is a complex task. Dempster-Shafer evidence theory (DSET) is widely used for handling such challenges due to its ability to model uncertainty and imprecision. However, Dempster's rule can sometimes yield counterintuitive results when dealing with highly conflicting evidence. In this paper, we introduce a novel belief sine similarity measure, called $BS^2M$, which effectively measures the discrepancy between different pieces of evidence. We also establish that $BS^2M$ possesses important properties such as boundedness, symmetry, and non-degeneracy. Building upon $BS^2M$, we present a new method for decision making. The proposed method considers both the credibility and the information volume of each evidence, providing a more comprehensive reflection of their importance. To validate our method, we conduct experiment in target recognition application, demonstrating the effectiveness and rationality of the proposed method.