Ordered Weighted Average Based Fuzzy Rough Sets


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dc.contributor.author Jensen, Richard
dc.contributor.author Verbiest, Nele
dc.contributor.author Cornelis, Chris
dc.date.accessioned 2010-10-15T08:33:38Z
dc.date.available 2010-10-15T08:33:38Z
dc.date.issued 2010-10
dc.identifier.citation Jensen , R , Verbiest , N & Cornelis , C 2010 , ' Ordered Weighted Average Based Fuzzy Rough Sets ' pp. 78-85 . en
dc.identifier.other PURE: 152154
dc.identifier.other dspace: 2160/5808
dc.identifier.uri http://hdl.handle.net/2160/5808
dc.identifier.uri http://www.springerlink.com/content/54r54161r345331w/ en
dc.description C. Cornelis, N. Verbiest and R. Jensen. Ordered Weighted Average Based Fuzzy Rough Sets. Proceedings of the 5th International Conference on Rough Sets and Knowledge Technology (RSKT2010), pp. 78-85, 2010. en
dc.description.abstract Traditionally, membership to the fuzzy-rough lower, resp. upper approximation is determined by looking only at the worst, resp. best performing object. Consequently, when applied to data analysis problems, these approximations are sensitive to noisy and/or outlying samples. In this paper, we advocate a mitigated approach, in which membership to the lower and upper approximation is determined by means of an aggregation process using ordered weighted average operators. In comparison to the previously introduced vaguely quantified rough set model, which is based on a similar rationale, our proposal has the advantage that the approximations are monotonous w.r.t. the used fuzzy indiscernibility relation. Initial experiments involving a feature selection application confirm the potential of the OWA-based model. en
dc.format.extent 8 en
dc.language.iso eng
dc.relation.ispartof en
dc.title Ordered Weighted Average Based Fuzzy Rough Sets 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 Peer reviewed en

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