Fuzzy-Rough Set based Semi-Supervised Learning

Math Trafodion Cynhadledd (Nid-Cyfnodolyn fathau)
Iaith wreiddiolSaesneg
Teitl2011 IEEE International Conference on Fuzzy Systems (FUZZ)
Nifer y tudalennau7
ISBN (Electronig)978-1-4244-7316-8
Dangosyddion eitem ddigidol (DOIs)
StatwsCyhoeddwyd - 06 Gorff 2011
Digwyddiad2011 IEEE International Conference on Fuzzy Systems - Taipei, Taiwan
Hyd: 27 Jun 201130 Jun 2011


Cynhadledd2011 IEEE International Conference on Fuzzy Systems
Cyfnod27 Jun 201130 Jun 2011
Arddangos ystadegau lawrlwytho
Gweld graff cysylltiadau
Fformatau enwi


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.