Paper Info
Title
An algebraic method and a genetic algorithm to the identification of fuzzy measures based on Choquet integrals
Abstract
There are different nonlinear integrals that could be used as an aggregation tool in information fusion and data mining. The Choquet integral with respect to fuzzy measures is one of them. We present some methods to identify fuzzy measures based on the Choquet integral in this paper. An iterative method introduced by Grabisch is discussed with some counterexamples. Furthermore, after removing some restrictions which are used in Grabisch's model, we introduce an algebraic method and a genetic algorithm to identify fuzzy measures and present some experimental results on both artificial and real-world data sets to show their effectiveness.
Year
DOI
Venue
2014
10.3233/IFS-130825
Journal of Intelligent and Fuzzy Systems
Keywords
DocType
Volume
real-world data,data mining,algebraic method,Choquet integral,fuzzy measure,different nonlinear integral,genetic algorithm,information fusion,aggregation tool,experimental result,iterative method
Journal
26
Issue
ISSN
Citations 
3
1064-1246
2
PageRank 
References 
Authors
0.38
7
2
Name
Order
Citations
PageRank
Naomi Kochi161.44
Zhenyuan Wang268490.22