A multi-granularity distance with its application for decision making

• Published in: Information sciences Vol. 661; p. 120168
• Main Authors: Zhao, Yangyang, Zhang, Zhanhao, Xiao, Fuyuan
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.03.2024
• Subjects: Basic belief assignments; Conflict management; Decision-making; Distance measure; Evidence theory; Multi-source information fusion; Decision-making; Basic belief assignments; Evidence theory; Conflict management; Multi-source information fusion; Distance measure
• ISSN: 0020-0255; 1872-6291
• DOI: 10.1016/j.ins.2024.120168

In decision-making systems, conflict management is an important concept that represents the degree of dissimilarity between bodies of evidence, ultimately enhancing decision-making performance. Jousselme's distance, as the most commonly employed one so far, is used to measure the distance between basic belief assignments (BBAs) in Dempster-Shafer (D-S) evidence theory. However, the Jousselme's distance has limitations, which can also be demonstrated in other methods theoretically. To address this issue, a BBA refinement method and a novel multi-granularity distance are proposed in this paper. Moreover, the methods are verified to be effective in the problems that Jousselme's distance cannot satisfy. Additionally, a hypothetical physical model is employed to verify the practicability of the proposed methods with multiple granularity. Furthermore, based on the proposed multiple granularity distance, a novel decision-making algorithm is designed. The results validate that the proposed decision-making method is beneficially applicable to classification scenarios and different real-world data.