A local mean-based k-nearest centroid neighbor classifier

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

*Corresponding author for this work

Research output: Contribution to journalArticle

48 Citations (Scopus)

Abstract

K-nearest neighbor (KNN) rule is a simple and effective algorithm in pattern classification. In this article, we propose a local mean-based k-nearest centroid neighbor classifier that assigns to each query pattern a class label with nearest local centroid mean vector so as to improve the classification performance. The proposed scheme not only takes into account the proximity and spatial distribution of k neighbors, but also utilizes the local mean vector of k neighbors from each class in making classification decision. In the proposed classifier, a local mean vector of k nearest centroid neighbors from each class for a query pattern is well positioned to sufficiently capture the class distribution information. In order to investigate the classification behavior of the proposed classifier, we conduct extensive experiments on the real and synthetic data sets in terms of the classification error. Experimental results demonstrate that our proposed method performs significantly well, particularly in the small sample size cases, compared with the state-of-the-art KNN-based algorithms.

Original languageEnglish
Pages (from-to)1058-1071
Number of pages14
JournalComputer Journal
Volume55
Issue number9
DOIs
Publication statusPublished - Sep 2012

Keywords

  • K-nearest centroid neighbor rule
  • K-nearest neighbor rule
  • local mean vector
  • nearest centroid neighborhood
  • pattern classification

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