Matrix Approaches for Covering-Based Multigranulation Fuzzy Rough Set Models. (4th January 2023)

• Record Type: Journal Article
• Authors: Chang, Zaibin; Wei, Junchao
• Other Names: Al-shami Tareq Academic Editor.
• Is Part Of: Journal of mathematics. Volume 2023(2023)
• Publication Date: 2023-01-04
• Subjects: Mathematics -- Periodicals; Mathematics; Periodicals; 510
• DOI: 10.1155/2023/3051168
• Languages: English
• ISSNs: 2314-4629

Abstract

Abstract : Multigranulation rough set theory is one of the most effective tools for data analysis and mining in multicriteria information systems. Six types of covering-based multigranulation fuzzy rough set (CMFRS) models have been constructed through fuzzy β -neighborhoods or multigranulation fuzzy measures. However, it is often time-consuming to compute these CMFRS models with a large fuzzy covering using set representation approaches. Hence, presenting novel methods to compute them quickly is our motivation for this paper. In this article, we study the matrix representations of CMFRS models to save time in data processing. Firstly, some new matrices and matrix operations are proposed. Then, matrix representations of optimistic CMFRSs are presented. Moreover, matrix approaches for computing pessimistic CMFRSs are also proposed. Finally, some experiments are proposed to illustrate the effectiveness of our approaches.