Fuzzy-Rough Set based Semi-Supervised Learning

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

16 Dyfyniadau(SciVal)
198 Wedi eu Llwytho i Lawr (Pure)


Much work has been carried out in the area of fuzzy-rough sets for supervised learning. However, very little has been accomplished for the unsupervised or semi-supervised tasks. For many real-word applications, it is often expensive, time-consuming and difficult to obtain labels for all data objects. This often results in large quantities of data which may only have very few labelled data objects. This paper proposes a novel fuzzy-rough based semi-supervised self-learning or self-training approach for the assignment of labels to unlabelled data. Unlike other semi-supervised approaches, the proposed technique requires no subjective thresholding or domain information. An experimental evaluation is performed on artificial data and also applied to a real-world mammographic risk assessment problem with encouraging results.
Iaith wreiddiolSaesneg
Teitl2011 IEEE International Conference on Fuzzy Systems (FUZZ)
Nifer y tudalennau7
ISBN (Electronig)978-1-4244-7316-8
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 06 Gorff 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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