Measures for Unsupervised Fuzzy-Rough Feature Selection

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dc.contributor.author Jensen, Richard
dc.contributor.author Mac Parthaláin, Neil
dc.date.accessioned 2010-01-11T10:24:56Z
dc.date.available 2010-01-11T10:24:56Z
dc.date.issued 2010-01-11
dc.identifier.citation Jensen , R & Mac Parthaláin , N 2010 , ' Measures for Unsupervised Fuzzy-Rough Feature Selection ' . en
dc.identifier.other PURE: 152129
dc.identifier.other dspace: 2160/3973
dc.identifier.uri http://hdl.handle.net/2160/3973
dc.description N. Mac Parthaláin, and R. Jensen. Measures for Unsupervised Fuzzy-Rough Feature Selection, Proceedings of the 9th International Conference on Intelligent Systems Design and Applications (ISDA'09), 2009. en
dc.description.abstract For supervised learning, feature selection algorithms attempt to maximise a given function of predictive accuracy. This function usually considers the ability of feature vectors to reflect decision class labels. It is therefore intuitive to retain only those features that are related to or lead to these decision classes. However, in unsupervised learning, decision class labels are not provided, which poses questions such as; which features should be retained? and, why not use all of the information? The problem is that not all features are important. Some of the features may be redundant, and others may be irrelevant and noisy. In this paper, some new fuzzy-rough set-based approaches to unsupervised feature selection are proposed. These approaches require no thresholding or domain information, and result in a significant reduction in dimensionality whilst retaining the semantics of the data. en
dc.language.iso eng
dc.title Measures for Unsupervised Fuzzy-Rough Feature Selection en
dc.type Text en
dc.type.publicationtype Conference paper en
dc.contributor.institution Department of Computer Science en
dc.description.status Non peer reviewed en


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