发表机构
School of Business, Central South University; School of Management, Shandong University（中南大学商学院; 山东大学管理学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对Dempster-Shafer证据融合的两大局限，提出含混沌-冲突度量与历史经验加权的统一框架，经16个数据集实验，其F1、AUC优于多种基线方法，可实现鲁棒分类。
AI 中文摘要
Dempster-Shafer（DST）理论下的多源证据融合面临两个长期存在的挑战：现有冲突度量独立评估证据间不一致性与证据内不确定性，导致评估不完整；现有融合方法仅通过瞬时比较评估证据源，未利用其在不同决策场景下的长期可靠性。本文提出统一的证据推理框架以解决这两个局限。具体而言，引入混沌-冲突度量联合量化证据间冲突与证据内非特异性，该度量具有5个经严格证明的属性，确保评估一致性；提出历史经验驱动的加权方案，通过谱聚类划分决策空间，并应用后悔理论从过往融合结果计算场景特异性可靠性分布。这些机制输入混合组合规则，该规则由全局冲突水平控制，自适应平衡不确定性保留与加权共识，随后采用信念区间决策策略，在不丢弃认知不确定性的情况下实现鲁棒分类。在16个真实世界基准数据集上的实验表明，所提框架的平均F1分数达85.78，平均AUC达93.30，优于8种基于DST的基线方法和3种梯度提升方法； ablation分析验证了所提各组件的贡献。该框架为多源决策中的自适应证据融合提供了有效途径。
英文摘要
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.
