Kernel-Based Fuzzy-Rough Nearest Neighbour Classification

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion CynhadleddTrafodion Cynhadledd (Nid-Cyfnodolyn fathau)

19 Dyfyniadau (Scopus)
223 Wedi eu Llwytho i Lawr (Pure)


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.
Iaith wreiddiolSaesneg
TeitlProceedings of the 20th IEEE International Conference on Fuzzy Systems
CyhoeddwrIEEE Press
Nifer y tudalennau7
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 06 Medi 2011
DigwyddiadFuzzy Systems - Taipei, Taiwan
Hyd: 27 Meh 201130 Meh 2011
Rhif y gynhadledd: 20


CynhadleddFuzzy Systems
Teitl crynoFUZZ-IEEE-2011
Cyfnod27 Meh 201130 Meh 2011

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