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      Distance-based classification methods

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      Author(s)
      Ekin, O.
      Hammer, P. L.
      Kogan, A.
      Winter, P.
      Date
      1999
      Source Title
      INFOR Journal
      Print ISSN
      0315-5986
      Publisher
      Taylor & Francis
      Volume
      37
      Issue
      3
      Pages
      337 - 352
      Language
      English
      Type
      Article
      Item Usage Stats
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      Abstract
      Given 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).
      Keywords
      Algorithms
      Data structures
      Learning systems
      Set theory
      Vectors
      Distance based classification method
      K-nearest neighbor
      Parzen windows
      Computational methods
      Permalink
      http://hdl.handle.net/11693/25223
      Published Version (Please cite this version)
      https://doi.org/10.1080/03155986.1999.11732388
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