Geometric interpretation of efficient weight vectors

IF 7.2 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Knowledge-Based Systems Pub Date : 2024-08-25 DOI:10.1016/j.knosys.2024.112403
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Abstract

Pairwise comparison matrices (PCMs) are frequently used in different multicriteria decision making problems. A weight vector is said to be efficient if no other weight vector is at least as good in estimating the elements of the PCM, and strictly better in at least one position. Understanding the efficient weight vectors is crucial to determine the appropriate weight calculation technique for a given problem. In this paper we study the set of efficient weight vectors for three and four dimensions (alternatives) from a geometric viewpoint, which is a complementary to the algebraic approach used in the literature. Besides providing well-interpretable demonstrations, we also draw attention to the particular role of weight vectors calculated from spanning trees. Weight vectors corresponding to line graphs are vertices of the (polyhedral, but usually nonconvex) set of efficient weight vectors, while weight vectors corresponding to other spanning trees are also on the boundary.

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有效权重向量的几何解释
成对比较矩阵(PCM)经常用于不同的多标准决策问题。如果在估算 PCM 的元素时,没有其他权重向量至少能达到同样的效果,而且至少在一个位置上严格来说更好,则称该权重向量为有效权重向量。了解高效权重向量对于确定特定问题的适当权重计算技术至关重要。在本文中,我们从几何角度研究了三维和四维(替代方案)的有效权重向量集,这是对文献中使用的代数方法的一种补充。除了提供易于理解的演示外,我们还提请注意从生成树计算出的权重向量的特殊作用。线图对应的权重向量是有效权重向量集(多面体,但通常是非凸的)的顶点,而其他生成树对应的权重向量也在边界上。
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来源期刊
Knowledge-Based Systems
Knowledge-Based Systems 工程技术-计算机:人工智能
CiteScore
14.80
自引率
12.50%
发文量
1245
审稿时长
7.8 months
期刊介绍: Knowledge-Based Systems, an international and interdisciplinary journal in artificial intelligence, publishes original, innovative, and creative research results in the field. It focuses on knowledge-based and other artificial intelligence techniques-based systems. The journal aims to support human prediction and decision-making through data science and computation techniques, provide a balanced coverage of theory and practical study, and encourage the development and implementation of knowledge-based intelligence models, methods, systems, and software tools. Applications in business, government, education, engineering, and healthcare are emphasized.
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