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C × K-Nearest Neighbor Classification with Ordered Weighted Averaging Distance

  • Gozde Ulutagay
  • Efendi Nasibov
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 586)

Abstract

In this study, OWA (Ordered Weighted Averaging) distance based C × K-nearest neighbor algorithm (C × K-NN) is considered. In this approach, from each class, where the number of classes is C, K-nearest neighbors are taken. The distance between the new sample and its K-nearest set is determined based on the OWA operator. It is shown that by adjusting the weights of the OWA operator, it is possible to obtain the results of various clustering strategies like single-linkage, complete-linkage, average-linkage, etc.

Keywords

Membership Degree Order Weight Average Order Weight Average Operator Order Weight Average Order Weight Average Weight 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

The authors would like to thank the anonymous reviewers for the constructive discussions and suggestions to improve the quality of this paper. This work is supported by TUBITAK (Scientific and Technological Research Council of Turkey) Grant No. 111T273.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Industrial EngineeringIzmir UniversityIzmirTurkey
  2. 2.Department of Computer ScienceDokuz Eylul UniversityIzmirTurkey
  3. 3.Institute of Cybernetics Azerbaijan National Academy of SciencesBaku]

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