Locally adaptive k parameter selection for nearest neighbor classifier: one nearest cluster

Bulut F., Amasyalı M. F.

PATTERN ANALYSIS AND APPLICATIONS, vol.20, no.2, pp.415-425, 2017 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 20 Issue: 2
  • Publication Date: 2017
  • Doi Number: 10.1007/s10044-015-0504-0
  • Page Numbers: pp.415-425
  • Keywords: Dynamic k parameter, k-NN, Classification, Clustering, Meta-parameter selection, CHOICE


The k nearest neighbors (k-NN) classification technique has a worldly wide fame due to its simplicity, effectiveness, and robustness. As a lazy learner, k-NN is a versatile algorithm and is used in many fields. In this classifier, the k parameter is generally chosen by the user, and the optimal k value is found by experiments. The chosen constant k value is used during the whole classification phase. The same k value used for each test sample can decrease the overall prediction performance. The optimal k value for each test sample should vary from others in order to have more accurate predictions. In this study, a dynamic k value selection method for each instance is proposed. This improved classification method employs a simple clustering procedure. In the experiments, more accurate results are found. The reasons of success have also been understood and presented.