Polytomous knowledge structures constructed by L-fuzzy approximation operators

IF 8.1 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences Pub Date : 2025-03-27 DOI:10.1016/j.ins.2025.122137

Bochi Xu , Jinjin Li , Fugui Shi

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引用次数: 0

Abstract

Rough set theory is more concerned with the character of the upper and lower approximations of a particular set than with the overall structure. Knowledge space theory can provide another new perspective on rough sets. In this paper, we establish a theoretical linkage between polytomous knowledge structures and L-fuzzy approximation operators. We generate polytomous knowledge structures constructed by L-fuzzy approximation operators and give the corresponding properties, and find that a polytomous knowledge space (polytomous closure space, respectively) and can be completely characterized by an upper (lower, respectively) L-fuzzy approximation. In particular, we discuss the dichotomous knowledge structure by the means of fuzzy approximation operators, which corresponds to the method of fuzzy skill maps. Finally, by L-fuzzy relation constructing two particular dichotomous knowledge structures, which are called backward-graded and forward-graded, is also discussed. This study proposes a framework to analyze L-fuzzy rough sets through knowledge space theory, bridging these mathematical disciplines.

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来源期刊

Information Sciences 工程技术-计算机：信息系统

CiteScore

14.00

自引率

17.30%

发文量

1322

审稿时长

10.4 months

期刊介绍： Informatics and Computer Science Intelligent Systems Applications is an esteemed international journal that focuses on publishing original and creative research findings in the field of information sciences. We also feature a limited number of timely tutorial and surveying contributions. Our journal aims to cater to a diverse audience, including researchers, developers, managers, strategic planners, graduate students, and anyone interested in staying up-to-date with cutting-edge research in information science, knowledge engineering, and intelligent systems. While readers are expected to share a common interest in information science, they come from varying backgrounds such as engineering, mathematics, statistics, physics, computer science, cell biology, molecular biology, management science, cognitive science, neurobiology, behavioral sciences, and biochemistry.