Fuzzy Entropy-Assisted Fuzzy-Rough Feature Selection

Allbwn ymchwil: Cyfraniad at gynhadleddPapur

24 Dyfyniadau(SciVal)
254 Wedi eu Llwytho i Lawr (Pure)


Feature Selection (FS) is a dimensionality reduction technique that aims to select a subset of the original features of a dataset which offer the most useful information. The benefits of feature selection include improved data visualisation, transparency, reduction in training and utilisation times and improved prediction performance. Methods based on fuzzy-rough set theory (FRFS) have employed the dependency function to guide the process with much success. This paper presents a novel fuzzy-rough FS technique which is guided by fuzzy entropy. The use of this measure in fuzzy-rough feature selection can result in smaller subset sizes than those obtained through FRFS alone, with little loss or even an increase in overall classification accuracy.
Iaith wreiddiolSaesneg
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 26 Gorff 2006
DigwyddiadFuzzy Systems - Vancouver, Canada
Hyd: 16 Gorff 200621 Gorff 2006
Rhif y gynhadledd: 15


CynhadleddFuzzy Systems
Teitl crynoFUZZ-IEEE-2006
Cyfnod16 Gorff 200621 Gorff 2006

Ôl bys

Gweld gwybodaeth am bynciau ymchwil 'Fuzzy Entropy-Assisted Fuzzy-Rough Feature Selection'. Gyda’i gilydd, maen nhw’n ffurfio ôl bys unigryw.

Dyfynnu hyn