Paper Info
Title
Attributes set reduction in multigranulation approximation space of a multi-source decision information system
Abstract
The multiple granular structures in multigranulation approximation space can be integrated by different approximation operators. These operators are induced by multigranulation rough set or others. In this paper, numerical algorithms of attributes set reduction are developed by using evidence theory or minimal elements of discernibility matrix, respectively. Firstly, a pair of multi-source rough approximation operators and their corresponding multigranulation rough approximation operators are defined based on a bijective and new transitional domain. Then, we propose attributes set reduction with respect to multi-source rough approximation. It is based on the relationship between multi-source rough approximations and evidence theory. Therefore, a heuristic algorithm of attributes set reduction for multi-source lower rough approximation is given. Secondly, we define a multigranulation variable precision rough set (MVRS) by considering weight of each attributes set relative to all of attributes sets. Finally, we investigate attributes set reduction with MVRS by combing the minimal elements of discernibility matrix and distribution discernibility function. There are some illustrative examples to elaborate the operation mechanism of above conclusions.
Year
DOI
Venue
2019
10.1007/s13042-018-0868-8
International Journal of Machine Learning and Cybernetics
Keywords
Field
DocType
Attributes set reduction, Evidence theory, Multigranulation variable precision rough set, Multi-source decision information system
Information system,Bijection,Matrix (mathematics),Heuristic (computer science),Algorithm,Rough set,Operator (computer programming),Combing,Multi-source,Mathematics
Journal
Volume
Issue
ISSN
10
9
1868-808X
Citations 
PageRank 
References 
1
0.35
24
Authors
2
Name
Order
Citations
PageRank
Xiaoya Che172.16
Ju-Sheng Mi2205477.81