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dc.contributor.authorEkin, O.en_US
dc.contributor.authorHammer, P. L.en_US
dc.contributor.authorKogan, A.en_US
dc.contributor.authorWinter, P.en_US
dc.date.accessioned2016-02-08T10:41:10Z
dc.date.available2016-02-08T10:41:10Z
dc.date.issued1999en_US
dc.identifier.issn0315-5986
dc.identifier.urihttp://hdl.handle.net/11693/25223
dc.description.abstractGiven a set of points in a Euclidean space, and a partitioning of this 'training set' into two or more subsets ('classes'), we consider the problem of identifying a 'reasonable' assignment of another point in the Euclidean space ('query point') to one of these classes. The various classifications proposed in this paper are determined by the distances between the query point and the points in the training set. We report results of extensive computational experiments comparing the new methods with two well-known distance-based classification methods (k-nearest neighbors and Parzen windows) on data sets commonly used in the literature. The results show that the performance of both new and old distance-based methods is on par with and often better than that of the other best classification methods known. Moreover, the new classification procedures proposed in this paper are: (i) easy to implement, (ii) extremely fast, and (iii) very robust (i.e. their performance is insignificantly affected by the choice of parameter values).en_US
dc.language.isoEnglishen_US
dc.source.titleINFOR Journalen_US
dc.relation.isversionofhttps://doi.org/10.1080/03155986.1999.11732388en_US
dc.subjectAlgorithmsen_US
dc.subjectData structuresen_US
dc.subjectLearning systemsen_US
dc.subjectSet theoryen_US
dc.subjectVectorsen_US
dc.subjectDistance based classification methoden_US
dc.subjectK-nearest neighboren_US
dc.subjectParzen windowsen_US
dc.subjectComputational methodsen_US
dc.titleDistance-based classification methodsen_US
dc.typeArticleen_US
dc.departmentDepartment of Industrial Engineering
dc.citation.spage337en_US
dc.citation.epage352en_US
dc.citation.volumeNumber37en_US
dc.citation.issueNumber3en_US
dc.identifier.doi10.1080/03155986.1999.11732388en_US
dc.publisherTaylor & Francisen_US


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