Approximation of basic probability assignment in dempster-shafer theory based on correlation coefficient

Yehang Shou, Xinyang Deng, Xiang Liu, Hanqing Zheng, Wen Jiang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

Dempster-Shafer (D-S) evidence theory is widely used for information fusion field. However, one of the main issues of D-S evidence theory is that, when large amount of focal elements in Basic Probability Assignment (BPA) are available, the fusion of BPA requires high computational cost and long computing time. This problem greatly limits its application. In this paper, a novel method for approximating a BPA based on correlation coefficient is present, which can reduce the computational cost of evidence combination effectively. At last, several numerical examples are given to illustrate the superiority of the proposed method.

Original languageEnglish
Title of host publication20th International Conference on Information Fusion, Fusion 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9780996452700
DOIs
StatePublished - 11 Aug 2017
Event20th International Conference on Information Fusion, Fusion 2017 - Xi'an, China
Duration: 10 Jul 201713 Jul 2017

Publication series

Name20th International Conference on Information Fusion, Fusion 2017 - Proceedings

Conference

Conference20th International Conference on Information Fusion, Fusion 2017
Country/TerritoryChina
CityXi'an
Period10/07/1713/07/17

Keywords

  • Approximation
  • Basic Probability Assignment
  • Belief functions
  • Correlation Coefficient
  • Dempster-Shafer theory

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