Developed cosine similarity measure on belief function theory: An application in medical diagnosis

Fereshteh Khalaj and Mehran Khalaj

Communications in Statistics - Theory and Methods, 2022, vol. 51, issue 9, 2858-2869

Abstract: In this study, we consider a new aspect of belief function or Dempster-Shafer theory to define a belief set and cosine similarity measure between two belief sets under uncertainty. For this purpose, firstly, the concept of belief sets will be represented as a triple vector space that is characterized by truth-belief degree, uncertainty-belief degree; and falsity-belief degree. Then, the cosine similarity measure between two belief sets is proposed to determine the degree of similarity between them. This measure is directly defined upon the framework of Dempster-Shafer theory without switching by other theories. Finally, an application of a new method in the decision-making process is provided in the medical diagnosis, when values were presented on the structure of belief set. Furthermore, a numerical example of the medical diagnosis is presented to show the effectiveness and flexibility of the proposed method.

Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2020.1782935 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:lstaxx:v:51:y:2022:i:9:p:2858-2869

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/lsta20

DOI: 10.1080/03610926.2020.1782935

Access Statistics for this article

Communications in Statistics - Theory and Methods is currently edited by Debbie Iscoe

More articles in Communications in Statistics - Theory and Methods from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().