A novel method for incremental feature selection with fuzzy β-covering

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

Fuzzy Sets and Systems Pub Date : 2025-03-24 DOI:10.1016/j.fss.2025.109379

Tianyu Wang, Shuai Liu, Bin Yang

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引用次数: 0

Abstract

Fuzzy β-covering has attracted significant academic attention due to its enhanced capability in representing uncertain information, surpassing traditional fuzzy covering approaches. However, the initial formulation of fuzzy β-covering rough sets fails to guarantee the inclusion relation between the upper and lower approximations. In addition, unlike partitions, coverings may contain redundant elements while still satisfying the covering property, making it crucial to assess whether any redundant elements are present. Nevertheless, the incremental mechanism for the reduct of fuzzy β-covering is still unclear. To address these limitations, we first introduce generalized fuzzy β-neighborhoods and derive the corresponding fuzzy β-covering rough sets, ensuring the inclusion relation between the upper and lower approximations. On this basis, a feature selection method with fuzzy β-covering based on relative discernibility relation is proposed, which only calculates the fuzzy positive region in the process of obtaining relative discernibility relation. Furthermore, to investigate incremental mechanisms for reduct of fuzzy β-covering, we develop novel incremental feature selection algorithms for fuzzy β-covering. Experimental comparisons with both non-incremental and incremental algorithms demonstrate that our proposed methods effectively identify the reduct of fuzzy β-covering, showcasing superior computational efficiency.

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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.