TY - JOUR
T1 - Weighted K-nearest centroid neighbor classification
AU - Gou, Jianping
AU - Du, Lan
AU - Xiong, Taisong
PY - 2012/2
Y1 - 2012/2
N2 - 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/
AB - 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/
KW - K-nearest centroid neighbor rule
KW - K-nearest neighbor rule
KW - Nearest centroid neighborhood
KW - Pattern classification
KW - Weighted voting
UR - http://www.scopus.com/inward/record.url?scp=84858831187&partnerID=8YFLogxK
M3 - Article
SN - 1553-9105
VL - 8
SP - 851
EP - 860
JO - Journal of Computational Information Systems
JF - Journal of Computational Information Systems
IS - 2
ER -