Robust Dempster-Shafer Evidence Fusion with Chaos-Conflict Measurement and Historical-Experience Weighting

2026-08-13

AI Summary

This work addresses the limitations of existing evidence fusion methods, which struggle to simultaneously capture inter-evidence conflict and intra-evidence uncertainty while neglecting the long-term reliability of evidence sources. To overcome these issues, the authors propose a unified evidential reasoning framework. It introduces a chaos-conflict joint measure satisfying five axioms to coherently quantify both conflict and nonspecificity. Furthermore, it incorporates a context-aware reliability assessment mechanism derived from historical fusion outcomes, leveraging spectral clustering and regret theory. This reliability estimate drives an adaptive combination rule and a belief-interval-based decision strategy. Evaluated on 16 real-world datasets, the method achieves an F1 score of 85.78% and an AUC of 93.30%, significantly outperforming eight Dempster–Shafer theory baselines and three gradient boosting approaches. Ablation studies confirm the contribution of each component.

Abstract

Multi-source evidence fusion under Dempster-Shafer theory faces two persistent challenges: existing conflict measures assess inter-evidence inconsistency and intra-evidence uncertainty independently, yielding incomplete evaluations, and current fusion methods evaluate evidence sources exclusively through instantaneous comparisns without exploiting their long-term reliability across diverse decision contexts. This paper proposes a unified evidence reasoning framework that addresses both limitations. Specifically, a chaos-conflict measurement is introduced to jointly quantify cross-evidence conflict and intra-evidence non-specificity, with five formally proven properties ensuring consistent assessment. A historical experience driven weighting scheme partitions the decision space via spectral clustering and applies regret theory to compute context-specific reliability profiles from past fusion outcomes. These mechanisms feed into a hybrid combination rule that adaptively balances uncertainty preservation against weighted consensus, controlled by the global conflict level, followed by a belief-interval decision strategy that enables robust classification without discarding epistemic uncertainty. Experiments on 16 real-world benchmark datasets demonstrate that the proposed framework achieves an average F1 score of 85.78 and a mean AUC of 93.30, outperforming eight DST-based baselines and three gradient boosting methods. Ablation analysis confirms the contribution of each component we proposed. The framework offers an effective approach for adaptive evidence fusion in multi-source decision making.

Problem

• evidence fusion
• conflict measurement
• Dempster-Shafer theory
• historical reliability
• multi-source decision making

Innovation

• Dempster-Shafer theory
• evidence fusion
• chaos-conflict measurement
• historical-experience weighting
• belief-interval decision