A new distance-weighted k-nearest neighbor classifier

Jianping Gou*, Lan Du, Yuhong Zhang, Taisong Xiong

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

172 Citations (Scopus)


In this paper, we develop a novel Distance-weighted k-nearest Neighbor rule (DWKNN), using the dual distance-weighted function. The proposed DWKNN is motivated by the sensitivity problem of the selection of the neighborhood size k that exists in k-nearest Neighbor rule (KNN), with the aim of improving classification performance. The experiment results on twelve real data sets demonstrate that our proposed classifier is robust to different choices of k to some degree, and yields good performance with a larger optimal k, compared to the other state-of-art KNN-based methods.

Original languageEnglish
Pages (from-to)1429-1436
Number of pages8
JournalJournal of Information and Computational Science
Issue number6
Publication statusPublished - Jun 2012


  • K-nearest neighbor rule
  • Pattern classification
  • Weighted voting


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