Novel fuzzy ?-covering rough set models and their applications

In recent years, fuzzy fl-covering, as a natural extension of fuzzy coverings, has attracted considerable attention. However, existing fuzzy fl-neighborhood operators cannot accu-rately describe the relationship between objects, which greatly restricts the application of fuzzy fl-covering. For this reason, we first construct four new fuzzy fl-neighborhood operators by using the existing fuzzy fl-neighborhood operator and generalized fuzzy logic operators, and investigate their properties. To better portray the similarity between sam-ples, inspired by the definition of fuzzy similarity relation, we define the concept of fuzzy fl-covering relation. On this basis, we develop a new framework of fuzzy fl-covering rough set models. We further propose an attribute reduction method by employing the new fuzzy fl-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.(c) 2022 Elsevier Inc. All rights reserved.