Paper Info
Title
Kernel-Based Fuzzy-Rough Nearest Neighbour Classification
Abstract
Fuzzy-rough sets play an important role in dealing with imprecision and uncertainty for discrete and real-valued or noisy data. However, there are some problems associated with the approach from both theoretical and practical viewpoints. These problems have motivated the hybridisation of fuzzy-rough sets with kernel methods. Existing work which hybridises fuzzy-rough sets and kernel methods employs a constraint that enforces the transitivity of the fuzzy T-norm operation. In this paper, such a constraint is relaxed and a new kernel-based fuzzy-rough set approach is introduced. Based on this, novel kernel-based fuzzy-rough nearest-neighbour algorithms are proposed. The work is supported by experimental evaluation, which shows that the new kernel-based methods offer improvements over the existing fuzzy-rough nearest neighbour classifiers. The abstract goes here.
Year
DOI
Venue
2011
10.1109/FUZZY.2011.6007401
IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ 2011)
Keywords
Field
DocType
Fuzzy-rough sets, Fuzzy tolerance relation, Kernel theory, Nearest neighbour classification
Data mining,Kernel smoother,Radial basis function kernel,Nearest neighbour classifiers,Kernel embedding of distributions,Computer science,Tree kernel,Fuzzy set,Polynomial kernel,Artificial intelligence,Kernel method,Machine learning
Conference
Volume
Issue
ISSN
null
null
1098-7584
Citations 
PageRank 
References 
4
0.41
8
Authors
5
Name
Order
Citations
PageRank
Yanpeng Qu1297.46
Changjing Shang221234.92
Qiang Shen3187894.48
Neil MacParthalain41628.85
Wei Wu530528.13