用证据决策法评估专家意见差异，提升医学图像诊断的可靠性
Inter Observer Variability Assessment through Ordered Weighted Belief Divergence Measure in MAGDM Application to the Ensemble Classifier Feature Fusion
- 引入置信度度量生成基础概率分配，捕捉每种方案特性
- 构建有序加权信念分歧度量，处理专家间冲突与不确定性
- 适用于医学影像融合诊断，尤其适合多专家共识场景
大量多属性群决策（MAGDM）方法被广泛用于获取共识结果。然而，多数方法忽略专家意见间的冲突，仅考虑同等或可变权重。为此，本文提出一种证据型MAGDM方法，通过评估专家间观察差异并处理其产生的不确定性。该框架有四方面贡献：第一，提出基础概率分配（BPA）生成方法，通过计算置信度来考虑每种方案的内在特征；第二，构建有序加权信念与可能性测度，通过评估专家间观察差异，捕获方案的整体内在信息并解决专家群体间的冲突；第三，构造有序加权信念分歧度量，获取各专家组的加权支持，以确定最终偏好关系；第四，通过一个实例验证所提框架，并分析其在真实世界中应用于集成分类器特征融合诊断视网膜疾病（基于光学相干断层扫描图像）的有效性。
原文摘要 · Abstract (English)
A large number of multi-attribute group decisionmaking (MAGDM) have been widely introduced to obtain consensus results. However, most of the methodologies ignore the conflict among the experts opinions and only consider equal or variable priorities of them. Therefore, this study aims to propose an Evidential MAGDM method by assessing the inter-observational variability and handling uncertainty that emerges between the experts. The proposed framework has fourfold contributions. First, the basic probability assignment (BPA) generation method is introduced to consider the inherent characteristics of each alternative by computing the degree of belief. Second, the ordered weighted belief and plausibility measure is constructed to capture the overall intrinsic information of the alternative by assessing the inter-observational variability and addressing the conflicts emerging between the group of experts. An ordered weighted belief divergence measure is constructed to acquire the weighted support for each group of experts to obtain the final preference relationship. Finally, we have shown an illustrative example of the proposed Evidential MAGDM framework. Further, we have analyzed the interpretation of Evidential MAGDM in the real-world application for ensemble classifier feature fusion to diagnose retinal disorders using optical coherence tomography images.
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