TY - JOUR
T1 - A hybrid intelligent data classification algorithm
AU - Yan, Xuesong
AU - Luo, Wenjing
AU - Wu, Qinghua
AU - Sheng, Victor S.
PY - 2013
Y1 - 2013
N2 - k-Nearest Neighbour (KNN) is one of the most popular algorithms for pattern recognition and data classification, but the traditional KNN classification method has some disadvantages. In this paper, aim at the KNN classification method's limitation, we proposed a hybrid intelligent classification algorithm. This novel algorithm combined the particle swarm optimisation algorithm and weighted KNN algorithm to improve classification performance. The experimental results show that our proposed algorithm outperforms the traditional KNN method with greater accuracy.
AB - k-Nearest Neighbour (KNN) is one of the most popular algorithms for pattern recognition and data classification, but the traditional KNN classification method has some disadvantages. In this paper, aim at the KNN classification method's limitation, we proposed a hybrid intelligent classification algorithm. This novel algorithm combined the particle swarm optimisation algorithm and weighted KNN algorithm to improve classification performance. The experimental results show that our proposed algorithm outperforms the traditional KNN method with greater accuracy.
KW - Data classification
KW - K-nearest neighbour
KW - Particle swarm optimisation
KW - Weighted k-nearest neighbour
UR - http://www.scopus.com/inward/record.url?scp=84888589243&partnerID=8YFLogxK
U2 - 10.1504/IJWMC.2013.057578
DO - 10.1504/IJWMC.2013.057578
M3 - Article
AN - SCOPUS:84888589243
SN - 1741-1084
VL - 6
SP - 573
EP - 580
JO - International Journal of Wireless and Mobile Computing
JF - International Journal of Wireless and Mobile Computing
IS - 6
ER -