Show simple item record Jensen, Richard Shen, Qiang
dc.contributor.editor Hassanien, Aboul-Ella
dc.contributor.editor Suraj, Zbigniew
dc.contributor.editor Slezak, Dominik
dc.contributor.editor Lingras, Pawan 2008-01-29T11:32:02Z 2008-01-29T11:32:02Z 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 PURE UUID: 3b310bce-b500-4875-a760-bad34427896b
dc.identifier.other dspace: 2160/490
dc.identifier.other DSpace_20121128.csv: row: 313
dc.identifier.other RAD: 2258
dc.identifier.other RAD_Outputs_All_ID_Import_20121105.csv: row: 1257
dc.identifier.other Scopus: 84899388090
dc.identifier.other 2160/490
dc.identifier.other ORCID: /0000-0002-1016-1524/work/57013153
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.rights en
dc.title Rough set based feature selection : A review en
dc.type /dk/atira/pure/researchoutput/researchoutputtypes/contributiontobookanthology/chapter en
dc.description.version authorsversion en
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Advanced Reasoning Group en

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