Conflicting evidence fusion using a correlation coefficient-based approach in complex network

Dempster–Shafer evidence theory (D–S theory) can effectively deal with uncertain information and it is one of the effective data fusion methods. However, Dempster’s combination rule of D–S theory often produces counter-intuitive fusion results when the handled body of evidence (BOE) is highly conflicting with each other. Therefore, many new methods have been gradually proposed to optimize BOE to avoid the counter-intuitive fusion results. In this work, inspired by the complex network, a body of evidence is compared to a node, therefore multiple nodes composed of the BOEs constitute a complex network structure, and a correlation coefficient is adopted to measure the degree of correlation between two BOEs. The direct and indirect interaction weights of each node are determined through the direct and indirect interactions among the nodes to reflect their importance in the complex network. After that, the total weight of each BOE is calculated through using the direct and indirect weights. Finally, after modifying the original BOE with weight factor, the final result is obtained after information fusion by using Dempster’s combination rule. This work analyses a practical application case based on the proposed evidential-weighting complex networks in D–S theory. The experiment result shows that the complex network optimization algorithm proposed in this work possesses a good convergence and has significantly improved the counter-intuitive fusion results brought about by the highly conflicting evidence with Dempster’s combination rule.