Release Time:2019-03-11  Hits:

Indexed by: Conference Paper

Date of Publication: 2011-06-27

Included Journals: Scopus、CPCI-S、EI

Page Number: 1523-1529

Key Words: Fuzzy-rough sets; Fuzzy tolerance relation; Kernel theory; 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.