Weighted K-nearest centroid neighbor classification

Jianping Gou*, Lan Du, Taisong Xiong

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    14 Citations (Scopus)


    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
    Issue number2
    Publication statusPublished - Feb 2012


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