Finding Fuzzy-rough Reducts with Fuzzy Entropy

Allbwn ymchwil: Cyfraniad at gynhadleddPapur

6 Dyfyniadau (Scopus)
153 Wedi eu Llwytho i Lawr (Pure)

Crynodeb

Dataset dimensionality is undoubtedly the single most significant obstacle which exasperates any attempt to apply effective computational intelligence techniques to problem domains. In order to address this problem a technique which reduces dimensionality is employed prior to the application of any classification learning.Such feature selection (FS) techniques attempt to select a subset of the original features of a dataset which are rich in the most useful information. The benefits can include improved data visualisation and transparency, a reduction in training and utilisation times and potentially, improved prediction performance. Methods based on fuzzy-rough set theory have demonstrated this with much success.Such methods have employed the dependency function which is based on the information contained in the lower approximation as an evaluation step in the FS process. This paper presents three novel feature selection techniques employing fuzzy entropy to locate fuzzy-rough reducts. This approach is compared with two other fuzzy-rough feature selection approaches which utilise other measures for the selection of subsets.
Iaith wreiddiolSaesneg
Tudalennau1282-1288
Nifer y tudalennau7
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 01 Meh 2008
DigwyddiadFuzzy Systems - Hong Kong, Hong Kong, Tsieina
Hyd: 01 Meh 200806 Meh 2008
Rhif y gynhadledd: 17

Cynhadledd

CynhadleddFuzzy Systems
Teitl crynoFUZZ-IEEE-2008
Gwlad/TiriogaethTsieina
DinasHong Kong
Cyfnod01 Meh 200806 Meh 2008

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