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<p>Dempster–Shafer evidence theory (D–S theory) is a well-known framework for reasoning under uncertainty. It can effectively combine information from multiple sources using Dempster’s rule of combination. However, when the information in the bodies of evidence (BOEs) is highly conflicting, applying Dempster’s rule directly can produce counterintuitive or unreliable results. To address this issue, we proposed a novel approach that models BOEs as nodes within a complex network. In this network, nodes (BOEs) were correlated with each other to varying degrees, quantified by classical evidence distance. The importance of each node was then determined by calculating its direct and indirect strength, both derived from these correlation degrees. Based on the strength measures, we assigned weight factors to adjust the belief degrees in the original evidence bodies. Finally, the weighted evidence was fused using Dempster’s combination rule. Experimental results demonstrated that our method effectively mitigates the limitations of the traditional rule, showing superior convergence speed and enhanced reliability when fusing highly conflicting evidence.</p>

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A Novel Reliability Model for Highly Conflicting Evidence Fusion with Complex Network Theory

  • 期刊论文

  • 发表日期：

    2026年6月20日

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  • Yongchuan Tang,
  • Zhou Gong,
  • Xuanming Lou,
  • Shijie Li,
  • He Guan,
  • Yubo Huang

摘要

Dempster–Shafer evidence theory (D–S theory) is a well-known framework for reasoning under uncertainty. It can effectively combine information from multiple sources using Dempster’s rule of combination. However, when the information in the bodies of evidence (BOEs) is highly conflicting, applying Dempster’s rule directly can produce counterintuitive or unreliable results. To address this issue, we proposed a novel approach that models BOEs as nodes within a complex network. In this network, nodes (BOEs) were correlated with each other to varying degrees, quantified by classical evidence distance. The importance of each node was then determined by calculating its direct and indirect strength, both derived from these correlation degrees. Based on the strength measures, we assigned weight factors to adjust the belief degrees in the original evidence bodies. Finally, the weighted evidence was fused using Dempster’s combination rule. Experimental results demonstrated that our method effectively mitigates the limitations of the traditional rule, showing superior convergence speed and enhanced reliability when fusing highly conflicting evidence.