Paper Info
Title
Rough Set Based Fuzzy Modeling by Occupancy Degree and Optimal Partition of Projection
Abstract
The rough set theory suggested by Pawlak has a property that it can represent the degree of consistency between condition and decision attributes of data pairs which don't have linguistic information. In this paper, by using this ability of rough set theory, we define a measure called occupancy degree which can represent a consistency degree of premise and consequent variables in fuzzy rules describing experimental data pairs. We also propose a method by which we partition the projected data on input space and find an optimal fuzzy rule table and membership functions of input and output variables from data without preliminary linguistic information.
Year
DOI
Venue
2004
10.1007/978-3-540-25929-9_37
Lecture Notes in Artificial Intelligence
Keywords
Field
DocType
membership function,rough set,rough set theory
Rule-based machine translation,Discrete mathematics,Computer science,Fuzzy logic,Fuzzy set,Rough set,Input/output,Partition (number theory),Membership function,Fuzzy rule
Conference
Volume
ISSN
Citations 
3066
0302-9743
0
PageRank 
References 
Authors
0.34
5
4
Name
Order
Citations
PageRank
Chang-Woo Park117421.54
Young-Wan Cho2817.40
Junhyuk Choi393.66
Ha-gyeong Sung482.96