Hybrid Fuzzy-Rough Rule Induction and Feature Selection

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dc.contributor.author Cornelis, Chris
dc.contributor.author Jensen, Richard
dc.contributor.author Shen, Qiang
dc.date.accessioned 2009-08-27T08:44:01Z
dc.date.available 2009-08-27T08:44:01Z
dc.date.issued 2009
dc.identifier.citation Cornelis , C , Jensen , R & Shen , Q 2009 , ' Hybrid Fuzzy-Rough Rule Induction and Feature Selection ' pp. 1151-1156 . en
dc.identifier.other PURE: 115230
dc.identifier.other dspace: 2160/2872
dc.identifier.uri http://hdl.handle.net/2160/2872
dc.description R. Jensen, C. Cornelis and Q. Shen. Hybrid Fuzzy-Rough Rule Induction and Feature Selection. Proceedings of the 18th International Conference on Fuzzy Systems (FUZZ-IEEE'09), pp. 1151-1156, 2009. en
dc.description.abstract The automated generation of feature pattern-based if-then rules is essential to the success of many intelligent pattern classifiers, especially when their inference results are expected to be directly human-comprehensible. Fuzzy and rough set theory have been applied with much success to this area as well as to feature selection. Since both applications of rough set theory involve the processing of equivalence classes for their successful operation, it is natural to combine them into a single integrated method that generates concise, meaningful and accurate rules. This paper proposes such an approach, based on fuzzy-rough sets. The algorithm is experimentally evaluated against leading classifiers, including fuzzy and rough rule inducers, and shown to be effective. en
dc.format.extent 6 en
dc.language.iso eng
dc.relation.ispartof en
dc.title Hybrid Fuzzy-Rough Rule Induction and Feature Selection 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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