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

• Published in: Chaos, solitons and fractals Vol. 176; p. 114087
• Main Authors: Tang, Yongchuan, Dai, Guoxun, Zhou, Yonghao, Huang, Yubo, Zhou, Deyun
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.11.2023
• Subjects: Complex network; Conflicting evidence fusion; Correlation coefficient; Dempster–Shafer evidence theory; Information fusion; Correlation coefficient; Conflicting evidence fusion; Dempster–Shafer evidence theory; Complex network; Information fusion
• ISSN: 0960-0779; 1873-2887
• DOI: 10.1016/j.chaos.2023.114087

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. •Conflicting evidence fusion using a correlation coefficient method.•Conflicting evidence fusion from the perspective of complex network.•The correlation between bodies of evidence is modeled with correlation coefficient.•The interactions of complex networks are used to model the weight of evidence.•The new method can obtain correct fusion results for conflicting evidence.