Search

The approximation space in rough set theory is important for dealing with uncertainties. As the information contained in various information systems is constantly updated and changed with the development of information technology, how to effectively obtain the approximation space in dynamic environments is essential. The local rough set as an excellent model avoids unnecessary calculation of information granules, and can significantly improve learning efficiency. In this paper, we mainly investigate a dynamic approximation update mechanism of multigranulation data from local viewpoint. We first define a support and inclusion function to construct local generalized multigranulation neighborhood rough set model. Then, the dynamic updating process of global rough set and local rough set is analyzed when object chandes. Meanwhile, the corresponding dynamic update algorithms for dynamic objects are proposed based on local generalized multigranulation rough set model. The complexity analysis about them theoretically proves the efficiency of local dynamic algorithm compared with global algorithm and static algorithm. To illustrate the effectiveness of proposed algorithms, twelve datasets from UCI are adopted to contrast experiments. [ABSTRACT FROM AUTHOR]

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. • 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. [ABSTRACT FROM AUTHOR]