Inter Observer Variability Assessment through Ordered Weighted Belief Divergence Measure in MAGDM Application to the Ensemble Classifier Feature Fusion

arXiv - MATH - Information Theory Pub Date : 2024-09-13 DOI:arxiv-2409.08450

Pragya GuptaDepartment of Mathematics Indian Institute of Technology Kharagpur, Debjani ChakrabortyDepartment of Mathematics Indian Institute of Technology Kharagpur, Debashree GuhaSchool of Medical Science and Technology Indian Institute of Technology Kharagpur

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Abstract

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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通过 MAGDM 中的有序加权信念发散度量评估观察者之间的变异性将其应用于集合分类器特征融合

为了获得协商一致的结果，人们广泛采用了大量多属性群体决策（MAGDM）方法。然而，大多数方法都忽略了专家意见之间的冲突，只考虑了专家的同等或不同优先级。因此，本研究旨在通过评估专家间的观测变异性和处理专家间出现的不确定性，提出一种可靠的 MAGDM 方法。所提出的框架有四个方面的贡献。首先，引入了基本概率分配（BPA）生成方法，通过计算信念度来考虑每个备选方案的固有特征。其次，构建了有序加权信念和可信度度量，通过评估观察间的可变性来捕捉替代方案的整体内在信息，并解决专家小组之间出现的冲突。最后，我们举例说明了所提出的保密 MAGDM 框架。此外，我们还分析了 Evidential MAGDM 在实际应用中的解释，即利用光学相干断层扫描图像诊断视网膜疾病的集合分类器特征融合。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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arXiv - MATH - Information Theory

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