由自反关系定义的粗糙集的结构

IF 3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Jouni Järvinen , Sándor Radeleczki
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引用次数: 0

摘要

对于几种类型的信息关系,诱导粗糙集系统RS不形成格而仅形成部分有序集。然而,通过研究其Dedekind-MacNeille补全DM(RS),可以揭示粗糙集结构新的重要性质。基于D. Umadevi在DM(RS)中描述连接和满足的工作,我们之前研究了DM(RS)上定义的用于自反关系的伪kleene代数。本文从自反关系的角度深入探讨了DM(RS)的序理论性质。描述了DM(RS)的完全连接不可约元,并刻画了DM(RS)是空间完全分布格的条件。我们证明了即使在非传递自反关系的情况下,DM(RS)也可以形成Nelson代数,这是一个通常与拟序相关的性质。我们引入了一个新的概念——关系邻域的核心,并利用它为DM(RS)确定Nelson代数提供了一个充分必要条件。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The structure of rough sets defined by reflexive relations
For several types of information relations, the induced rough sets system RS does not form a lattice but only a partially ordered set. However, by studying its Dedekind–MacNeille completion DM(RS), one may reveal new important properties of rough set structures. Building upon D. Umadevi's work on describing joins and meets in DM(RS), we previously investigated pseudo-Kleene algebras defined on DM(RS) for reflexive relations. This paper delves deeper into the order-theoretic properties of DM(RS) in the context of reflexive relations. We describe the completely join-irreducible elements of DM(RS) and characterize when DM(RS) is a spatial completely distributive lattice. We show that even in the case of a non-transitive reflexive relation, DM(RS) can form a Nelson algebra, a property generally associated with quasiorders. We introduce a novel concept, the core of a relational neighbourhood, and use it to provide a necessary and sufficient condition for DM(RS) to determine a Nelson algebra.
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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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