Show simple item record Jensen, Richard Shen, Qiang 2009-08-27T08:43:52Z 2009-08-27T08:43:52Z 2009-08-27
dc.identifier.citation Jensen , R & Shen , Q 2009 , ' Interval-valued Fuzzy-Rough Feature Selection in Datasets with Missing Values ' pp. 610-615 . en
dc.identifier.other PURE: 124131
dc.identifier.other dspace: 2160/2871
dc.description R. Jensen and Q. Shen. Interval-valued Fuzzy-Rough Feature Selection in Datasets with Missing Values. Proceedings of the 18th International Conference on Fuzzy Systems (FUZZ-IEEE'09), pp. 610-615, 2009. 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 principle 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 in Datasets with Missing Values 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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