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dc.contributor.author Jensen, Richard
dc.contributor.author Mac Parthaláin, Neil
dc.contributor.author Shen, Qiang
dc.date.accessioned 2008-07-03T10:41:07Z
dc.date.available 2008-07-03T10:41:07Z
dc.date.issued 2008-07-03
dc.identifier.citation Jensen , R , Mac Parthaláin , N & Shen , Q 2008 , ' Finding Fuzzy-Rough Reducts with Fuzzy Entropy ' . en
dc.identifier.other PURE: 85440
dc.identifier.other dspace: 2160/598
dc.identifier.uri http://hdl.handle.net/2160/598
dc.description N. Mac Parthaláin, R. Jensen, and Q. Shen. Finding Fuzzy-Rough Reducts with Fuzzy Entropy. Proceedings of the 2008 IEEE Conference on Fuzzy Systems, Hong Kong. 2008 en
dc.description.abstract Dataset dimensionality is undoubtedly the single most significant obstacle which exasperates any attempt to apply effective computational intelligence techniques to problem domains. In order to address this problem a technique which reduces dimensionality is employed prior to the application of any classification learning. Such feature selection (FS) techniques attempt to select a subset of the original features of a dataset which are rich in the most useful information. The benefits can include improved data visualisation and transparency, a reduction in training and utilisation times and potentially, improved prediction performance. Methods based on fuzzy-rough set theory have demonstrated this with much success. Such methods have employed the dependency function which is based on the information contained in the lower approximation as an evaluation step in the FS process. This paper presents three novel feature selection techniques employing fuzzy entropy to locate fuzzy-rough reducts. This approach is compared with two other fuzzy-rough feature selection approaches which utilise other measures for the selection of subsets. en
dc.language.iso eng
dc.title Finding Fuzzy-Rough Reducts with Fuzzy Entropy 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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