模糊描述轮廓上比较异同关系的加权和 Choquet $$L^p$$ 距离表示法

IF 1.2 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Annals of Mathematics and Artificial Intelligence Pub Date : 2024-01-24 DOI:10.1007/s10472-024-09924-y
Giulianella Coletti, Davide Petturiti, Bernadette Bouchon-Meunier
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引用次数: 0

摘要

我们考虑的是模糊描述轮廓上成对对象的比较异同关系,后者提供了成对对象的基于模糊集的表示方法。这种关系表达了 "不比......更不相似 "的概念,决策者在模糊信息下执行基于案例的决策任务时会用到它。我们首先局限于那些允许加权(\varvec{L}^p\)距离表示的关系,对于这些关系,我们提供了一个公理化的表征,以防该关系是完整的、传递性的,并且定义在模糊描述轮廓对的整个空间上。接下来,我们转而讨论更一般的比较不相似性关系,这种比较不相似性关系可以用 Choquet \(\varvec{L}^p\) 距离表示,其参数是完全交替的归一化容量。
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Weighted and Choquet $$L^p$$ distance representation of comparative dissimilarity relations on fuzzy description profiles

We consider comparative dissimilarity relations on pairs on fuzzy description profiles, the latter providing a fuzzy set-based representation of pairs of objects. Such a relation expresses the idea of “no more dissimilar than” and is used by a decision maker when performing a case-based decision task under vague information. We first limit ourselves to those relations admitting a weighted \(\varvec{L}^p\) distance representation, for which we provide an axiomatic characterization in case the relation is complete, transitive and defined on the entire space of pairs of fuzzy description profiles. Next, we switch to the more general class of comparative dissimilarity relations representable by a Choquet \(\varvec{L}^p\) distance, parameterized by a completely alternating normalized capacity.

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来源期刊
Annals of Mathematics and Artificial Intelligence
Annals of Mathematics and Artificial Intelligence 工程技术-计算机:人工智能
CiteScore
3.00
自引率
8.30%
发文量
37
审稿时长
>12 weeks
期刊介绍: Annals of Mathematics and Artificial Intelligence presents a range of topics of concern to scholars applying quantitative, combinatorial, logical, algebraic and algorithmic methods to diverse areas of Artificial Intelligence, from decision support, automated deduction, and reasoning, to knowledge-based systems, machine learning, computer vision, robotics and planning. The journal features collections of papers appearing either in volumes (400 pages) or in separate issues (100-300 pages), which focus on one topic and have one or more guest editors. Annals of Mathematics and Artificial Intelligence hopes to influence the spawning of new areas of applied mathematics and strengthen the scientific underpinnings of Artificial Intelligence.
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