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Robust Dempster-Shafer Evidence Fusion with Chaos-C... | AI Research

Key Takeaways

  • This paper introduces a unified evidence reasoning framework designed to improve how multi-source information is combined under Dempster-Shafer theory (DST)....
  • 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.
  • Ablation analysis confirms the contribution of each component we proposed.
Paper AbstractExpand

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.

This paper introduces a unified evidence reasoning framework designed to improve how multi-source information is combined under Dempster-Shafer theory (DST). It addresses two primary weaknesses in existing fusion methods: the inability to jointly evaluate conflict and uncertainty, and the reliance on instantaneous comparisons that ignore the long-term reliability of evidence sources.

Addressing Fusion Challenges

Traditional DST-based fusion often struggles when evidence sources provide conflicting or imprecise information. Existing conflict measures typically treat inter-evidence inconsistency and intra-evidence uncertainty as separate issues, leading to incomplete evaluations. Furthermore, current methods evaluate sources based only on their immediate performance, failing to account for how a source has behaved across different decision contexts in the past. This can lead to the suppression of generally reliable sources that are occasionally conflicting, or the failure to penalize systematically unreliable ones.

The Proposed Framework

The authors propose a framework consisting of two core mechanisms:

  • Chaos-Conflict Measurement (CCM): This tool provides a single scalar quantity that evaluates both cross-evidence association and intra-evidence non-specificity. By doing so, it captures the internal structure of multi-element focal elements—which represent ignorance—rather than just measuring total disagreement. The authors provide formal proofs for five properties of this measure, including boundedness and symmetry, to ensure consistent assessment.

  • Historical-Experience Weighting: This scheme uses spectral clustering to partition the decision space into specific contexts. It then applies regret theory to analyze past fusion outcomes. By calculating "regret" (when an evidence source steers an ensemble away from the truth) and "rejoice" (when it pulls the ensemble toward the truth), the framework assigns context-specific reliability weights to each source based on their long-term track record.

Performance and Results

The framework integrates these mechanisms into a hybrid combination rule that adaptively balances uncertainty preservation against weighted consensus, depending on the level of global conflict. A belief-interval decision strategy is then used for classification.
In experiments conducted on 16 real-world benchmark datasets, the framework achieved an average F1 score of 85.78 and a mean AUC of 93.30. These results outperformed eight DST-based baselines and three gradient boosting methods. Ablation analysis indicated that the historical experience weighting component was the most significant contributor to performance, with its removal resulting in a 5.03% reduction in AUC.

Considerations for Implementation

The framework is designed for multi-source decision-making environments where evidence sources are used repeatedly across heterogeneous contexts. While the authors demonstrate that the approach is robust against feature noise, label noise, and varying numbers of evidence sources, the effectiveness of the historical weighting relies on the availability of past fusion outcomes to build reliability profiles. The authors note that this approach is applicable to any setting where evidence sources operate across diverse, recurring decision scenarios.

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