A fuzzy β-covering rough set model for attribute reduction by composite measure

IF 3.2 1区数学 Q2 COMPUTER SCIENCE, THEORY & METHODS

Fuzzy Sets and Systems Pub Date : 2025-02-14 DOI:10.1016/j.fss.2025.109314

Xiongtao Zou , Jianhua Dai

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引用次数: 0

Abstract

Fuzzy β-covering rough sets, the state-of-the-art theory of covering-based rough sets, have received much attention. The key step in establishing a fuzzy rough set model is to construct the fuzzy binary relation between objects. However, under the environment of multiple fuzzy β-coverings, the existing fuzzy β-neighborhoods either do not satisfy the reflexivity of relation or rely on R-implication operators. For this reason, these fuzzy β-neighborhoods are not very suitable for characterizing the similarity between objects. In order to characterize the similarity between objects under the environment of multiple fuzzy β-coverings more reasonably, this paper constructs a β-neighborhood that satisfies the reflexivity and does not depend on any fuzzy operators. On this basis, a novel fuzzy β-covering rough set model is established. Based on the proposed model, several monotonic uncertainty measures of fuzzy β-coverings are defined. Finally, an attribute reduction method is presented, and the experimental analysis demonstrates the effectiveness of our proposed method compared with the other five excellent attribute reduction methods.

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来源期刊

Fuzzy Sets and Systems 数学-计算机：理论方法

CiteScore

6.50

自引率

17.90%

发文量

321

审稿时长

6.1 months

期刊介绍： Since its launching in 1978, the journal Fuzzy Sets and Systems has been devoted to the international advancement of the theory and application of fuzzy sets and systems. The theory of fuzzy sets now encompasses a well organized corpus of basic notions including (and not restricted to) aggregation operations, a generalized theory of relations, specific measures of information content, a calculus of fuzzy numbers. Fuzzy sets are also the cornerstone of a non-additive uncertainty theory, namely possibility theory, and of a versatile tool for both linguistic and numerical modeling: fuzzy rule-based systems. Numerous works now combine fuzzy concepts with other scientific disciplines as well as modern technologies. In mathematics fuzzy sets have triggered new research topics in connection with category theory, topology, algebra, analysis. Fuzzy sets are also part of a recent trend in the study of generalized measures and integrals, and are combined with statistical methods. Furthermore, fuzzy sets have strong logical underpinnings in the tradition of many-valued logics.