Weighted K-nearest centroid neighbor classification

Jianping Gou*, Lan Du, Taisong Xiong

*Corresponding author for this work

Research output: Contribution to journalArticle

13 Citations (Scopus)

Abstract

The k-Nearest Centroid Neighbor rule (KNCN), as an extension of the k-Nearest Neighbor rule (KNN), is one of the promising algorithms in pattern classification. In this article, we take into consideration the proximity and spatial distribution of the neighbors by means of nearest centroid neighborhood for a query pattern, and introduce two weighted voting schemes for KNCN. Experimental results show that the proposed classifiers are effective algorithms, and obtain much improvement over the state-of-the-art KNN based algorithms. 1553-9105/

Original languageEnglish
Pages (from-to)851-860
Number of pages10
JournalJournal of Computational Information Systems
Volume8
Issue number2
Publication statusPublished - Feb 2012

Keywords

  • K-nearest centroid neighbor rule
  • K-nearest neighbor rule
  • Nearest centroid neighborhood
  • Pattern classification
  • Weighted voting

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