A novel belief χ2 ${χ }^{2}$ divergence for multisource information fusion and its application in pattern classification - Zhang, Lang; Xiao, Fuyuan (International journal of intelligent systems, 2022)
Dempster–Shafer (D‐S) evidence theory is invaluable in the domain of multisource information fusion for handing uncertainty problems. However, there may be counter‐intuitive phenomenon when facing highly conflicting information. In this paper, a novel symmetric enhanced belief χ2 ${χ }^{2}$ divergence measure, called S E B χ2 $SEB{χ }^{2}$, is proposed to measure the discrepancy between basic probability assignments (BPAs). The S E B χ2 $SEB{χ }^{2}$ divergence consider the features of BPAs as the influence of both single‐element subsets and multielement subsets is taken into account. Furthermore, the S E B χ2 $SEB{χ }^{2}$ divergence is proven to be symmetric, nonnegative and nondegenerate, which are desirable properties for conflict management. Then, a new algorithm for multisource information fusion based on the S E B χ2 $SEB{χ }^{2}$ divergence measure is derived. Finally, an application for pattern classification is used to illustrate the superiority of the proposed S E B χ2 $SEB{χ }^{2}$ divergence measure‐based fusion method over other existing well‐known and recent related works with a better classification accuracy of 94.39%.