Evidential Reasoning With Divisive Hierarchical Clustering for Multisource Information Fusion

Abstract

Dempster–Shafer (DS) evidence theory provides a powerful framework for modeling uncertainty, reasoning, and combining information from multiple sources. However, it may yield counterintuitive 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 intracluster 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 intracluster fusion via classical Dempster’s rule and intercluster 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 multisource information fusion system, with experimental results demonstrating improved performance of target classification.