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dc.contributor.author Shen, Qiang
dc.contributor.author Jensen, Richard
dc.date.accessioned 2013-07-23T13:42:13Z
dc.date.available 2013-07-23T13:42:13Z
dc.date.issued 2008
dc.identifier.citation Shen , Q & Jensen , R 2008 , Interval-valued Fuzzy-Rough Feature Selection and Application for Handling Missing Values in Datasets . in 8th Annual UK Workshop on Computational Intelligence (UKCI'08) . pp. 59-64 , 8th Annual UK Workshop on Computational Intelligence (UKCI'08) , Leicester , United Kingdom of Great Britain and Northern Ireland , 10 Sep 2008 . en
dc.identifier.citation conference en
dc.identifier.other PURE: 238472
dc.identifier.other PURE UUID: e03666d9-8b4d-4a49-b3a0-dc8ee4c2726e
dc.identifier.other RAD: 2225
dc.identifier.other DSpace_20121128.csv: row: 454
dc.identifier.other handle.net: 2160/11035
dc.identifier.other ORCID: /0000-0002-1016-1524/work/57013140
dc.identifier.uri http://hdl.handle.net/2160/11035
dc.description R. Jensen and Q. Shen. Interval-valued Fuzzy-Rough Feature Selection and Application for Handling Missing Values in Datasets. Proceedings of the 8th Annual UK Workshop on Computational Intelligence (UKCI'08), pp. 59-64, 2008. en
dc.description.abstract One of the many successful applications of rough set theory has been to the area of feature selection. The rough set ideology of using only the supplied data and no other information has many benefits, where most other methods require supplementary knowledge. Fuzzy-rough set theory has recently been proposed as an extension of this, in order to better handle the uncertainty present in real data. However, following this approach, there has been no investigation (theoretical or otherwise) into how to deal with missing values effectively, another problem encountered when using real world data. This paper proposes an extension of the fuzzy-rough feature selection methodology, based on interval-valued fuzzy sets, as a means to counter this problem via the representation of missing values in an intuitive way. en
dc.format.extent 6 en
dc.language.iso eng
dc.relation.ispartof 8th Annual UK Workshop on Computational Intelligence (UKCI'08) en
dc.rights en
dc.title Interval-valued Fuzzy-Rough Feature Selection and Application for Handling Missing Values in Datasets en
dc.type /dk/atira/pure/researchoutput/researchoutputtypes/contributiontobookanthology/conference en
dc.description.version preprint en
dc.description.version preprint en
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Advanced Reasoning Group en


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