Show simple item record Tuson, Andrew Shen, Qiang Jensen, Richard 2010-07-19T15:47:04Z 2010-07-19T15:47:04Z 2010
dc.identifier.citation Tuson , A , Shen , Q & Jensen , R 2010 , ' Extending Propositional Satisfiability to Determine Minimal Fuzzy-Rough Reducts ' pp. 1415-1422 . en
dc.identifier.other PURE: 150146
dc.identifier.other PURE UUID: 922e35a6-3c55-489b-ad9a-3979965ec96a
dc.identifier.other dspace: 2160/4825
dc.identifier.other DSpace_20121128.csv: row: 3620
dc.identifier.other RAD: 5612
dc.identifier.other RAD_Outputs_All_ID_Import_20121105.csv: row: 2398
dc.identifier.other Scopus: 78549270111
dc.description R. Jensen, A. Tuson and Q. Shen. Extending Propositional Satisfiability to Determine Minimal Fuzzy-Rough Reducts. Proceedings of the 19th International Conference on Fuzzy Systems (FUZZ-IEEE’10), pp. 1415-1422, 2010. en
dc.description.abstract This paper describes a novel, principled approach to real-valued dataset reduction based on fuzzy and rough set theory. The approach is based on the formulation of fuzzy-rough discernibility matrices, that can be transformed into a satisfiability problem; an extension of rough set approaches that only apply to discrete datasets. The fuzzy-rough hybrid reduction method is then realised algorithmically by a modified version of a traditional satisifability approach. This produces an efficient and provably optimal approach to data reduction that works well on a number of machine learning benchmarks in terms of both time and classification accuracy. en
dc.format.extent 8 en
dc.language.iso eng
dc.relation.ispartof en
dc.rights en
dc.title Extending Propositional Satisfiability to Determine Minimal Fuzzy-Rough Reducts en
dc.type /dk/atira/pure/researchoutput/researchoutputtypes/contributiontoconference/paper en
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Advanced Reasoning Group en
dc.description.status Peer reviewed en

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