Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/40756
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dc.titleFuzzy-rough nearest neighbors algorithm
dc.contributor.authorSarkar, Manish
dc.date.accessioned2013-07-04T08:11:36Z
dc.date.available2013-07-04T08:11:36Z
dc.date.issued2000
dc.identifier.citationSarkar, Manish (2000). Fuzzy-rough nearest neighbors algorithm. Proceedings of the IEEE International Conference on Systems, Man and Cybernetics 5 : 3556-3561. ScholarBank@NUS Repository.
dc.identifier.issn08843627
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/40756
dc.description.abstractIn this paper the classification efficiency of the conventional K-nearest neighbors algorithm is enhanced by exploiting the fuzzy-rough uncertainty. The simplicity and nonparametric characteristics of the conventional K-nearest neighbors algorithm remain intact in the proposed algorithm. Unlike the conventional one, the proposed algorithm does not need to know the optimal value of K. Moreover, the generated class confidence values, which are interpreted in terms of the fuzzy-rough ownership values, do not necessarily summed up to one. Consequently, the proposed algorithm can distinguish between equal evidence and ignorance, and thus makes the semantics of the class confidence values richer.
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.sourcetitleProceedings of the IEEE International Conference on Systems, Man and Cybernetics
dc.description.volume5
dc.description.page3556-3561
dc.description.codenPICYE
dc.identifier.isiutNOT_IN_WOS
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