Show simple item record Cornelis, Chris Jensen, Richard Hurtado, Germán Slezak, Dominik 2009-12-15T14:07:39Z 2009-12-15T14:07:39Z 2010-01-15
dc.identifier.citation Cornelis , C , Jensen , R , Hurtado , G & Slezak , D 2010 , ' Attribute Selection with Fuzzy Decision Reducts ' Information Sciences , vol 180 , no. 2 , pp. 209-224 . DOI: 10.1016/j.ins.2009.09.008 en
dc.identifier.issn 0020-0255
dc.identifier.other PURE: 142383
dc.identifier.other PURE UUID: 8901e405-eb2c-43f2-aefb-7fb58aa0afc2
dc.identifier.other dspace: 2160/3824
dc.description Cornelis, C., Jensen, R., Hurtado, G., Slezak, D. (2010). Attribute Selection with Fuzzy Decision Reducts. Information Sciences, 180 (2), 209-224 en
dc.description.abstract Rough set theory provides a methodology for data analysis based on the approximation of concepts in information systems. It revolves around the notion of discernibility: the ability to distinguish between objects, based on their attribute values. It allows to infer data dependencies that are useful in the fields of feature selection and decision model construction. In many cases, however, it is more natural, and more effective, to consider a gradual notion of discernibility. Therefore, within the context of fuzzy rough set theory, we present a generalization of the classical rough set framework for data-based attribute selection and reduction using fuzzy tolerance relations. The paper unifies existing work in this direction, and introduces the concept of fuzzy decision reducts, dependent on an increasing attribute subset measure. Experimental results demonstrate the potential of fuzzy decision reducts to discover shorter attribute subsets, leading to decision models with a better coverage and with comparable, or even higher accuracy. en
dc.format.extent 16 en
dc.language.iso eng
dc.relation.ispartof Information Sciences en
dc.rights en
dc.subject Rough sets en
dc.subject Fuzzy sets en
dc.subject Attribute selection en
dc.subject Data analysis en
dc.subject Decision reducts en
dc.title Attribute Selection with Fuzzy Decision Reducts en
dc.type /dk/atira/pure/researchoutput/researchoutputtypes/contributiontojournal/article 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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