K Nearest Neighbor Classification with Local Induction of the Simple Value Difference Metric

  • Andrzej Skowron
  • Arkadiusz Wojna
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3066)


The classical k nearest neighbor (k-nn) classification assumes that a fixed global metric is defined and searching for nearest neighbors is always based on this global metric. In the paper we present a model with local induction of a metric. Any test object induces a local metric from the neighborhood of this object and selects k nearest neighbors according to this locally induced metric. To induce both the global and the local metric we use the weighted Simple Value Difference Metric (SVDM). The experimental results show that the proposed classification model with local induction of a metric reduces classification error up to several times in comparison to the classical k-nn method.


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© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Andrzej Skowron
    • 1
  • Arkadiusz Wojna
    • 1
  1. 1.Faculty of Mathematics, Informatics and MechanicsWarsaw UniversityWarsawPoland

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