Multi-granulation-based optimal scale selection in multi-scale information systems. (June 2021)

• Record Type: Journal Article
• Authors: Wang, Haoran; Li, Wentao; Zhan, Tao; Yuan, Kehua; Hu, Xingchen
• Is Part Of: Computers & electrical engineering. Volume 92(2021)
• Publication Date: 2021-06
• Subjects: Deep learning -- Knowledge discovery -- Multi-granulation rough set -- Multi-scale -- Optimal scale selection; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107107
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: The notions of multi-granulation and multi-scale are two important issues for the granular computing, both of which can describe the granular structure in certain ways. In this paper, we investigate the belief structure and the plausibility structure by defining belief and plausibility functions from the multi-granulation viewpoint, and discuss how to construct multi-granulation rough set (MGRS) models in multi-scale information systems (MSISs). Based on the MGRS in MSISs, the optimal scale selection methods with various requirements are studied in two aspects of optimistic and pessimistic multi-granulation for a multi-scale decision information system (MSDIS). To interpret and understand the proposed theories, some important properties of optimistic and pessimistic multi-granulation optimal scale selection are analyzed for the MSDIS, which could indicate the inner relationships among the different selection methods of the optimal scales. Furthermore, an example is provided to illustrate and verify these investigated properties. Highlights: Multigranulation-based belief and plausibility functions and structures are proposed. Multigranulation rough sets in multi-scale information systems are presented. Multigranulation-based optimal scale selection for multi-scale information systems are discussed. Important properties of multigranulation-based optimal scale selection are analyzed.