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dc.contributor.author Jensen, Richard
dc.contributor.author Shen, Qiang
dc.contributor.editor Hassanien, Aboul-Ella
dc.contributor.editor Suraj, Zbigniew
dc.contributor.editor Slezak, Dominik
dc.contributor.editor Lingras, Pawan
dc.date.accessioned 2008-01-29T11:32:02Z
dc.date.available 2008-01-29T11:32:02Z
dc.date.issued 2007-11-15
dc.identifier.citation Jensen , R & Shen , Q 2007 , ' Rough set based feature selection : A review ' . in A-E Hassanien , Z Suraj , D Slezak & P Lingras (eds) , Rough Computing : Theories, Technologies and Applications . Information Science Reference , pp. 70-107 . en
dc.identifier.isbn 978-1599045528
dc.identifier.other PURE: 74125
dc.identifier.other dspace: 2160/490
dc.identifier.uri http://hdl.handle.net/2160/490
dc.description.abstract Feature selection aims to determine a minimal feature subset from a problem domain while retaining a suitably high accuracy in representing the original features. Rough set theory (RST) has been used as such a tool with much success. RST enables the discovery of data dependencies and the reduction of the number of attributes contained in a dataset using the data alone, requiring no additional information. This chapter describes the fundamental ideas behind RST-based approaches and reviews related feature selection methods that build on these ideas. Extensions to the traditional rough set approach are discussed, including recent selection methods based on tolerance rough sets, variable precision rough sets and fuzzy-rough sets. Alternative search mechanisms are also highly important in rough set feature selection. The chapter includes the latest developments in this area, including RST strategies based on hill-climbing, genetic algorithms and ant colony optimization. en
dc.format.extent 38 en
dc.language.iso eng
dc.publisher Information Science Reference
dc.relation.ispartof Rough Computing en
dc.title Rough set based feature selection : A review en
dc.type Text en
dc.type.publicationtype Book chapter en
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Advanced Reasoning Group en


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