Paper Info
Title
Measures of general fuzzy rough sets on a probabilistic space
Abstract
Fuzzy rough set is a generalization of crisp rough set, which deals with both fuzziness and vagueness in data. The measures of fuzzy rough sets aim to dig its numeral characters in order to analyze data effectively. In this paper we first develop a method to compute the cardinality of fuzzy set on a probabilistic space, and then propose a real number valued function for each approximation operator of the general fuzzy rough sets on a probabilistic space to measure its approximate accuracy. The functions of lower and upper approximation operators are natural generalizations of the belief function and plausibility function in Dempster-Shafer theory of evidence, respectively. By using these functions, accuracy measure, roughness degree, dependency function, entropy and conditional entropy of general fuzzy rough set are proposed, and the relative reduction of fuzzy decision system is also developed by using the dependency function and characterized by the conditional entropy. At last, these measure functions for approximation operators are characterized by axiomatic approaches.
Year
DOI
Venue
2008
10.1016/j.ins.2008.03.020
Inf. Sci.
Keywords
DocType
Volume
fuzzy rough set,fuzzy set,crisp rough set,fuzzy decision system,probabilistic space,general fuzzy rough set,conditional entropy,belief function,approximation operator,dependency function,relative reduction,value function,rough set
Journal
178
Issue
ISSN
Citations 
16
0020-0255
22
PageRank 
References 
Authors
0.67
21
3
Name
Order
Citations
PageRank
Degang Chen1150045.65
Wenxia Yang2220.67
Fachao Li315722.30