Fuzzy-Rough Nearest Neighbour Classification and Prediction

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dc.contributor.author Cornelis, Chris
dc.contributor.author Jensen, Richard
dc.date.accessioned 2011-09-07T08:57:31Z
dc.date.available 2011-09-07T08:57:31Z
dc.date.issued 2011-09-07
dc.identifier.citation Cornelis , C & Jensen , R 2011 , ' Fuzzy-Rough Nearest Neighbour Classification and Prediction ' Theoretical Computer Science , vol 412 , no. 42 , pp. 5871-5884 . en
dc.identifier.other PURE: 170507
dc.identifier.other dspace: 2160/7564
dc.identifier.uri http://hdl.handle.net/2160/7564
dc.description R. Jensen and C. Cornelis. Fuzzy-Rough Nearest Neighbour Classification and Prediction. Theoretical Computer Science, vol. 412, no. 42, pp. 5871-5884, 2011. en
dc.description.abstract Nearest neighbour (NN) approaches are inspired by the way humans make decisions, comparing a test object to previously encountered samples. In this paper, we propose an NN algorithm that uses the lower and upper approximations from fuzzy-rough set theory in order to classify test objects, or predict their decision value. It is shown experimentally that our method outperforms other NN approaches (classical, fuzzy and fuzzy-rough ones) and that it is competitive with leading classification and prediction methods. Moreover, we show that the robustness of our methods against noise can be enhanced effectively by invoking the approximations of the Vaguely Quantified Rough Set (VQRS) model, which emulates the linguistic quantifiers “some” and “most” from natural language. en
dc.format.extent 14 en
dc.language.iso eng
dc.relation.ispartof Theoretical Computer Science en
dc.title Fuzzy-Rough Nearest Neighbour Classification and Prediction en
dc.type Text en
dc.type.publicationtype Article (Journal) en
dc.identifier.doi http://dx.doi.org/10.1016/j.tcs.2011.05.040
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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