用直觉模糊集提升证据可靠性评估，改善复杂场景分类决策
Evaluating Evidential Reliability In Pattern Recognition Based On Intuitionistic Fuzzy Sets
- 结合模糊集与证据理论，通过决策贡献量化证据可信度
- 在多个数据集上优于传统证据融合与机器学习方法
- 适合高冲突、不确定性强的分类决策场景
在证据理论（DST）中，评估证据来源的可靠性至关重要。以往方法采用折减法处理证据间的高冲突，但可能影响分类模型效率。本文提出一种基于直觉模糊集（IFS）的证据可靠性量化算法——模糊可靠性指数（FRI）。该算法基于IFS的决策量化规则，定义不同基本概率分配（BPA）对正确决策的贡献，并由此推导出证据可靠性。实验表明，该方法显著提升了可靠性估计的合理性，在复杂场景分类任务中表现优异。与基于DST的算法及经典机器学习方法对比，FRI展现出更强的优越性与通用性。本研究为未来证据源可靠性分析和概率转换提供了新思路。
原文摘要 · Abstract (English)
Determining the reliability of evidence sources is a crucial topic in Dempster-Shafer theory (DST). Previous approaches have addressed high conflicts between evidence sources using discounting methods, but these methods may not ensure the high efficiency of classification models. In this paper, we consider the combination of DS theory and Intuitionistic Fuzzy Sets (IFS) and propose an algorithm for quantifying the reliability of evidence sources, called Fuzzy Reliability Index (FRI). The FRI algorithm is based on decision quantification rules derived from IFS, defining the contribution of different BPAs to correct decisions and deriving the evidential reliability from these contributions. The proposed method effectively enhances the rationality of reliability estimation for evidence sources, making it particularly suitable for classification decision problems in complex scenarios. Subsequent comparisons with DST-based algorithms and classical machine learning algorithms demonstrate the superiority and generalizability of the FRI algorithm. The FRI algorithm provides a new perspective for future decision probability conversion and reliability analysis of evidence sources.
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