Authors: Xuesong Yan; Wenjing Luo; Qinghua Wu; Victor S. Sheng
Addresses: School of Computer Science, China University of Geosciences, Wuhan, Hubei 430074, China ' School of Computer Science, China University of Geosciences, Wuhan, Hubei 430074, China ' Hubei Provincial Key Laboratory of Intelligent Robot, School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan, Hubei 430073, China ' Department of Computer Science, University of Central Arkansas, Conway, AR 72035, USA
Abstract: 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.
Keywords: intelligent data classification; k-nearest neighbour; weighted kNN; particle swarm optimisation; PSO; classification performance; classification accuracy.
International Journal of Wireless and Mobile Computing, 2013 Vol.6 No.6, pp.573 - 580
Received: 04 Jun 2013
Accepted: 03 Jul 2013
Published online: 12 Nov 2013 *