A weighted graph network-based method for combining conflicting evidence

Abstract

Information fusion technology is crucial in intricate information systems, and Dempster–Shafer evidence(DSE) theory plays a significant role in it. However, most of the current research focuses on improving the conflict measurement method of high-conflict evidence in the DSE theory framework, while ignoring the comprehensive consideration of multiple conflicts of complex information. Considering the generality of graph network to complex system modeling, novel evidence measurement factors (EMF) and weighted Graph Convolution Network Dempster–Shafer evidence (wGCNDS) combination method, are proposed to optimize the combination of conflict evidence from the perspective of graph network. By constructing a weighted graph network, information transmission is realized and information fusion of associated nodes is completed. Numerical examples and real datasets verify the effectiveness and performance of wGCNDS.