Evidential Reasoning With Divisive Hierarchical Clustering for Multisource Information Fusion
Kezhu Zuo, Xinde Li, Le Yu, Kaixuan Wu, Siyuan Li, Yilin Dong, Zhijun Li · IEEE Transactions on Fuzzy Systems · 2025
Dempster-Shafer (DS) evidence theory provides a powerful framework for modeling uncertainty, reasoning, and combining information from multiple sources. However, it may yield counter-intuitive results when handling conflicting evidence, thereby affecting decision reliability and limiting practical applications. To address this issue, this work proposes a novel Evidential Reasoning rule with Divisive Hierarchical Clustering (ER-DHC), consisting of two main modules: evidence clustering and cluster fusion. At first, a new divisive hierarchical algorithm is introduced for evidence clustering, comprising coarse-grained and fine-grained division. In the coarse-grained stage, evidence with different decision preferences is grouped into separate clusters, thus preventing high intra-cluster conflicts and laying a solid foundation for evidence clustering. The fine-grained division adaptively refines cluster structures using an inflection point detection method, thereby enhancing clustering quality. On this basis, a new cluster fusion strategy is developed, involving intra-cluster fusion via classical Dempster's rule and inter-cluster fusion using a fuzzy preference relation-based weighted approach. This fusion strategy can degenerate into classical DS fusion and weighted fusion, while also introducing a new clustering fusion perspective, offering better flexibility. Finally, the proposed ER-DHC method is applied to the multi-source information fusion system, with experimental results demonstrating improved performance of target classification.