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dc.contributor.author Shen, Qiang
dc.contributor.author Jensen, Richard
dc.date.accessioned 2008-09-16T10:23:28Z
dc.date.available 2008-09-16T10:23:28Z
dc.date.issued 2008-09-16
dc.identifier.citation Shen , Q & Jensen , R 2008 , ' Interval-valued Fuzzy-Rough Feature Selection and Application for Handling Missing Values in Datasets ' pp. 59-64 . en
dc.identifier.other PURE: 86221
dc.identifier.other dspace: 2160/646
dc.identifier.uri http://hdl.handle.net/2160/646
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 en
dc.title Interval-valued Fuzzy-Rough Feature Selection and Application for Handling Missing Values in Datasets 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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