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dc.contributor.author MacParthaláin, Neil Seosamh
dc.contributor.author Jensen, Richard
dc.contributor.author Shen, Qiang
dc.date.accessioned 2014-03-31T20:02:14Z
dc.date.available 2014-03-31T20:02:14Z
dc.date.issued 2008-06-01
dc.identifier.citation MacParthaláin , N S , Jensen , R & Shen , Q 2008 , ' Finding Fuzzy-rough Reducts with Fuzzy Entropy ' Paper presented at IEEE International Conference on Fuzzy Systems, 2008 , China , 01 Jun 2008 - 06 Jun 2008 , pp. 1282-1288 . https://doi.org/10.1109/FUZZY.2008.4630537 en
dc.identifier.citation conference en
dc.identifier.other PURE: 4911224
dc.identifier.other PURE UUID: dbba95c1-a211-4340-8686-e396fdef07b4
dc.identifier.other RAD: 2222
dc.identifier.other DSpace_20121128.csv: row: 426
dc.identifier.other Scopus: 55249111065
dc.identifier.other handle.net: 2160/13647
dc.identifier.other ORCID: /0000-0002-1016-1524/work/57013159
dc.identifier.other ORCID: /0000-0003-1935-2914/work/60054322
dc.identifier.uri http://hdl.handle.net/2160/13647
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.format.extent 7 en
dc.language.iso eng
dc.relation.ispartof en
dc.rights en
dc.title Finding Fuzzy-rough Reducts with Fuzzy Entropy en
dc.type /dk/atira/pure/researchoutput/researchoutputtypes/contributiontoconference/paper en
dc.description.version preprint en
dc.identifier.doi https://doi.org/10.1109/FUZZY.2008.4630537
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Advanced Reasoning Group en
dc.description.status Non peer reviewed en


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