A New Approach to Fuzzy-Rough Nearest Neighbour Classification

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dc.contributor.author Jensen, Richard
dc.contributor.author Cornelis, Chris
dc.date.accessioned 2008-10-17T08:10:06Z
dc.date.available 2008-10-17T08:10:06Z
dc.date.issued 2008-10-17
dc.identifier.citation Jensen , R & Cornelis , C 2008 , ' A New Approach to Fuzzy-Rough Nearest Neighbour Classification ' . in 6th International Conference on Rough Sets and Current Trends in Computing, LNAI . pp. 310-319 . en
dc.identifier.other PURE: 357868
dc.identifier.other dspace: 2160/674
dc.identifier.uri http://hdl.handle.net/2160/674
dc.description R. Jensen and C. Cornelis. A New Approach to Fuzzy-Rough Nearest Neighbour Classification. Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing, LNAI 5306, pp. 310-319, 2008. en
dc.description.abstract In this paper, we present a new fuzzy-rough nearest neighbour (FRNN) classification algorithm, as an alternative to Sarkar’s fuzzy-rough ownership function (FRNN-O) approach. By contrast to the latter, our method uses the nearest neighbours to construct lower and upper approximations of decision classes, and classifies test instances based on their membership to these approximations. In the experimental analysis, we evaluate our approach with both classical fuzzy-rough approximations (based on an implicator and a t-norm), as well as with the recently introduced vaguely quantified rough sets. Preliminary results are very good, and in general FRNN outperforms both FRNN-O, as well as the traditional fuzzy nearest neighbour (FNN) algorithm. en
dc.format.extent 10 en
dc.language.iso eng
dc.relation.ispartof 6th International Conference on Rough Sets and Current Trends in Computing, LNAI en
dc.title A New Approach to Fuzzy-Rough Nearest Neighbour Classification en
dc.type Text en
dc.type.publicationtype Conference proceeding en
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Advanced Reasoning Group en


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