Belief Exponential Divergence for D-S Evidence Theory and its Application in Multi-Source Information Fusion
Year: 2024
Type: article
Abstract: Dempster-Shafer evidence theory is broadly employed in the research of multi-source information fusion. Nevertheless, when fusing highly conflicting evidence it may produce counterintuitive outcomes. To address this issue, a fusion approach based on a newly defined belief exponential divergence and Deng entropy is proposed. First, a belief exponential divergence is proposed as the conflict measurement between evidences. Then, the credibility of each evidence is calculated. Afterwards, the Deng entropy is used to calculate information volume to determine the uncertainty of evidence. Then, the weight of evidence is calculated by integrating the credibility and uncertainty of each evidence. Ultimately, initial evidences are amended and fused using Dempster's rule of combination. The effectiveness of this approach in addressing the fusion of three typical conflict paradoxes is demonstrated by arithmetic examples. Additionally, the proposed approach is applied to aerial target recognition and iris dataset-based classification to validate its efficacy. Results indicate that the proposed approach can enhance the accuracy of target recognition and effectively address the issue of fusing conflicting evidences.
Authors: Xiaobo Duan, Qiucen Fan, Wenhao Bi, An Zhang
Institution: Northwestern Polytechnical University
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: diamond
Awards: G3290271426, G8783966174
OpenAlex ID: W4406658058