Show simple item record Jensen, Richard Tuson, Andrew Shen, Qiang 2013-11-22T21:07:45Z 2013-11-22T21:07:45Z 2014-01-10
dc.identifier.citation Jensen , R , Tuson , A & Shen , Q 2014 , ' Finding rough and fuzzy-rough set reducts with SAT ' Information Sciences , vol. 255 , pp. 100-120 . en
dc.identifier.issn 0020-0255
dc.identifier.other PURE: 3454944
dc.identifier.other PURE UUID: 506ed006-ad94-40e4-a7d3-1ab0ab0860ed
dc.identifier.other Scopus: 84886089024
dc.identifier.other 2160/12775
dc.identifier.other ORCID: /0000-0002-1016-1524/work/57013143
dc.description.abstract Feature selection refers to the problem of selecting those input features that are most predictive of a given outcome; a problem encountered in many areas such as machine learning, pattern recognition and signal processing. In particular, solution to this has found successful application in tasks that involve datasets containing huge numbers of features (in the order of tens of thousands), which would otherwise be impossible to process further. Recent examples include text processing and web content classification. Rough set theory has been used as such a dataset pre-processor with much success, but current methods are inadequate at finding globally minimal reductions, the smallest sets of features possible. This paper proposes a technique that considers this problem from a propositional satisfiability perspective. In this framework, globally minimal subsets can be located and verified. en
dc.language.iso eng
dc.relation.ispartof Information Sciences en
dc.rights en
dc.subject Rough set theory en
dc.subject Fuzzy rough set theory en
dc.subject Feature selection en
dc.subject Boolean satisfiability en
dc.title Finding rough and fuzzy-rough set reducts with SAT en
dc.type /dk/atira/pure/researchoutput/researchoutputtypes/contributiontojournal/article en
dc.description.version authorsversion en
dc.contributor.institution Department of Computer Science en
dc.description.status Peer reviewed en

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