A novel axiomatic approach to L-valued rough sets within an L-universe via inner product and outer product of L-subsets

IF 3.2 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Lingqiang Li, Qiu Jin
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引用次数: 0

Abstract

The fuzzy rough approximation operator serves as the cornerstone of fuzzy rough set theory and its practical applications. Axiomatization is a crucial approach in the exploration of fuzzy rough sets, aiming to offer a clear and direct characterization of fuzzy rough approximation operators. Among the fundamental tools employed in this process, the inner product and outer product of fuzzy sets stand out as essential components in the axiomatization of fuzzy rough sets. In this paper, we will develop the axiomatization of a comprehensive fuzzy rough set theory, that is, the so-called L-valued rough sets with an L-set serving as the foundational universe (referred to as the L-universe) for defining L-valued rough approximation operators, where L typically denotes a GL-quantale. Firstly, we give the notions of inner product and outer product of two L-subsets within an L-universe and examine their basic properties. It is shown that these notions are extensions of the corresponding notion of fuzzy sets within a classical universe. Secondly, leveraging the inner product and outer product of L-subsets, we respectively characterize L-valued upper and lower rough approximation operators generated by general, reflexive, transitive, symmetric, Euclidean, and median L-value relations on L-universe as well as their compositions. Finally, utilizing the provided axiomatic characterizations, we present the precise examples for the least and largest equivalent L-valued upper and lower rough approximation operators. Notably, many existing axiom characterizations of fuzzy rough sets within classical universe can be viewed as direct consequences of our findings.
利用l子集的内积和外积研究l域内l值粗糙集的一种新的公理化方法
模糊粗糙逼近算子是模糊粗糙集理论及其实际应用的基础。公理化是研究模糊粗糙集的一种重要方法,其目的是提供模糊粗糙逼近算子清晰、直接的表征。在此过程中使用的基本工具中,模糊集合的内积和外积是模糊粗糙集公理化的重要组成部分。在本文中,我们将发展一个综合模糊粗糙集理论的公理化,即所谓的L值粗糙集,其中L集作为定义L值粗糙逼近算子的基本域(简称L域),其中L通常表示一个gl -量子化。首先给出了l -域内两个l子集的内积和外积的概念,并研究了它们的基本性质。证明了这些概念是经典宇宙中模糊集的相应概念的扩展。其次,利用l子集的内积和外积,分别刻画了l -宇宙上由一般、自反、传递、对称、欧几里得和中位l值关系产生的l值上、下粗逼近算子及其组成。最后,利用所提供的公理刻画,给出了最小和最大等价l值上下粗糙逼近算子的精确例子。值得注意的是,经典宇宙中存在的许多模糊粗糙集的公理刻画可以看作是我们的发现的直接结果。
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来源期刊
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning 工程技术-计算机:人工智能
CiteScore
6.90
自引率
12.80%
发文量
170
审稿时长
67 days
期刊介绍: The International Journal of Approximate Reasoning is intended to serve as a forum for the treatment of imprecision and uncertainty in Artificial and Computational Intelligence, covering both the foundations of uncertainty theories, and the design of intelligent systems for scientific and engineering applications. It publishes high-quality research papers describing theoretical developments or innovative applications, as well as review articles on topics of general interest. Relevant topics include, but are not limited to, probabilistic reasoning and Bayesian networks, imprecise probabilities, random sets, belief functions (Dempster-Shafer theory), possibility theory, fuzzy sets, rough sets, decision theory, non-additive measures and integrals, qualitative reasoning about uncertainty, comparative probability orderings, game-theoretic probability, default reasoning, nonstandard logics, argumentation systems, inconsistency tolerant reasoning, elicitation techniques, philosophical foundations and psychological models of uncertain reasoning. Domains of application for uncertain reasoning systems include risk analysis and assessment, information retrieval and database design, information fusion, machine learning, data and web mining, computer vision, image and signal processing, intelligent data analysis, statistics, multi-agent systems, etc.
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