Fuzzy-Rough Set based Semi-Supervised Learning

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dc.contributor.author MacParthalain, Neil
dc.contributor.author Jensen, Richard
dc.date.accessioned 2011-07-06T09:29:52Z
dc.date.available 2011-07-06T09:29:52Z
dc.date.issued 2011-07-06
dc.identifier.citation MacParthalain , N & Jensen , R 2011 , ' Fuzzy-Rough Set based Semi-Supervised Learning ' . in 2011 IEEE International Conference on Fuzzy Systems (FUZZ) . pp. 2465-2472 , 2011 IEEE International Conference on Fuzzy Systems , Taipei , Taiwan , 27-30 June . en
dc.identifier.citation conference en
dc.identifier.isbn 978-1-4244-7315-1
dc.identifier.isbn 978-1-4244-7316-8
dc.identifier.other PURE: 1178211
dc.identifier.other dspace: 2160/7137
dc.identifier.uri http://hdl.handle.net/2160/7137
dc.description N. Mac Parthalain and R. Jensen.Fuzzy-Rough Set based Semi-Supervised Learning. Proceedings of the 20th International Conference on Fuzzy Systems (FUZZ-IEEE’11), pp. 2465-2471, 2011. en
dc.description.abstract 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. en
dc.format.extent 7 en
dc.language.iso eng
dc.relation.ispartof 2011 IEEE International Conference on Fuzzy Systems (FUZZ) en
dc.title Fuzzy-Rough Set based Semi-Supervised Learning en
dc.type Text en
dc.type.publicationtype Conference proceeding en
dc.identifier.doi http://dx.doi.org/10.1109/FUZZY.2011.6007483
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Vision, Graphics and Visualisation Group en


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