Show simple item record Cornelis, Chris Jensen, Richard Shen, Qiang 2009-08-27T08:44:01Z 2009-08-27T08:44:01Z 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 PURE UUID: 30d07fb0-07e3-4c30-8d3b-6300f8be9379
dc.identifier.other dspace: 2160/2872
dc.identifier.other DSpace_20121128.csv: row: 2173
dc.identifier.other RAD: 609
dc.identifier.other RAD_Outputs_All_ID_Import_20121105.csv: row: 328
dc.identifier.other Scopus: 71249144807
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.rights en
dc.title Hybrid Fuzzy-Rough Rule Induction and Feature Selection 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 Non peer reviewed en

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