Paper Info
Title
Multi-granulation Rough Sets and Uncertainty Measurement for Multi-source Fuzzy Information System.
Abstract
Rough set theory is an effective mathematical tool to deal with uncertain information. With the arrival of the information age, we need to handle not only single-source data sets, but also multi-source data sets. In real life; most of the data we face are fuzzy multi-source data sets. However, the rough set model has not been found for multi-source fuzzy information systems. This paper aims to study how to use the rough set model in multi-source fuzzy environment. Firstly, we define a distance formula between two objects in an information table and further propose a tolerance relation through this formula. Secondly, the supporting characteristic function is proposed by the inclusion relation between tolerance classes and concept set X. And then, from the perspective of multi-granulation, each information source is regarded as a granularity. The optimistic, pessimistic, generalized multi-granulation rough set model and some important properties are discussed in multi-source fuzzy information systems. At the same time, the uncertainty measurement are considered for the different models. Finally, some experiments are carried out to interpret and evaluate the validity and significance of the approach.
Year
DOI
Venue
2019
10.1007/s40815-019-00667-1
International Journal of Fuzzy Systems
Keywords
Field
DocType
Multi-source fuzzy information system, Generalized multi-granulation, Optimistic multi-granulation, Pessimistic multi-granulation, Uncertainty measurement
Information system,Data mining,Data set,Mathematical optimization,Characteristic function (probability theory),Fuzzy logic,Measurement uncertainty,Rough set,Granularity,Multi-source,Mathematics
Journal
Volume
Issue
ISSN
21
6
1562-2479
Citations 
PageRank 
References 
2
0.36
0
Authors
4
Name
Order
Citations
PageRank
Lei Yang110511.42
Xiaoyan Zhang220.36
Weihua Xu334823.88
Binbin Sang4376.26