Paper Info
Title
Generic extended multigranular sets for mixed and incomplete information systems
Abstract
Granular computing is a widely used computational paradigm nowadays. Particularly, within the rough set theory, granular computing plays a key role. In this paper, we propose a generic approach of rough sets, the granular extended multigranular sets (GEMS) for dealing with both mixed and incomplete information systems. Not only our proposal does use the traditional optimistic and pessimistic granulations with respect to single attributes, but also we introduce granulations with respect to attribute sets, as well as two new ways of granulating: the optimistic + pessimistic granulation and the pessimistic + optimistic granulation. In addition, we have developed a particular case of the proposed GEMS: the multigranular maximum similarity rough sets (MMSRS). We have proved some of the properties of the MMSRS, and we tested its effectiveness with respect to other existing granular rough sets models. The experimental results show the flexibility and the capabilities of the proposed model, while handling mixed and incomplete information systems.
Year
DOI
Venue
2020
10.1007/s00500-020-04748-4
Soft Computing
Keywords
DocType
Volume
Rough sets, Multigranularity, Incomplete information systems, Mixed information systems
Journal
24
Issue
ISSN
Citations 
8
1432-7643
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Yenny Villuendas-rey14614.38
Cornelio Yáñez-Márquez215326.34
José Luis Velázquez-Rodríguez300.34