Novel fuzzy β-covering rough set models and their applications

In recent years, fuzzy β -covering, as a natural extension of fuzzy coverings, has attracted considerable attention. However, existing fuzzy β -neighborhood operators cannot accurately describe the relationship between objects, which greatly restricts the application of fuzzy β -covering. For this reason, we first construct four new fuzzy β -neighborhood operators by using the existing fuzzy β -neighborhood operator and generalized fuzzy logic operators, and investigate their properties . To better portray the similarity between samples, inspired by the definition of fuzzy similarity relation, we define the concept of fuzzy β -covering relation. On this basis, we develop a new framework of fuzzy β -covering rough set models. We further propose an attribute reduction method by employing the new fuzzy β -covering relation, and design a heuristic attribute reduction algorithm with reference to an uncertainty measure called attribute significance. Finally, experimental results show the superiority of our proposed method through a series of experimental analyses.