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dc.contributor.author Shen, Qiang
dc.contributor.author Tuson, Andrew
dc.contributor.author Jensen, Richard
dc.date.accessioned 2008-01-21T16:56:32Z
dc.date.available 2008-01-21T16:56:32Z
dc.date.issued 2005
dc.identifier.citation Shen , Q , Tuson , A & Jensen , R 2005 , ' Finding Rough Set Reducts with SAT. ' pp. 194-203 . en
dc.identifier.other PURE: 74218
dc.identifier.other dspace: 2160/443
dc.identifier.uri http://hdl.handle.net/2160/443
dc.description R. Jensen, Q. Shen and A. Tuson, 'Finding Rough Set Reducts with SAT,' Proceedings of the 10th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, LNAI 3641, pp. 194-203, 2005. en
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 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 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, minimal subsets can be located and verified. An initial experimental investigation is conducted, comparing the new method with a standard rough set-based feature selector. en
dc.format.extent 10 en
dc.language.iso eng
dc.relation.ispartof en
dc.title Finding Rough Set Reducts with SAT. en
dc.type Text en
dc.type.publicationtype Conference paper en
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Advanced Reasoning Group en
dc.description.status Non peer reviewed en


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