Authors: Yonghua Wang; Jingyi Lu; Kaidi Zhao
Addresses: School of Shangmao, Zhejiang Technical Institute of Economics, Hangzhou, Zhejiang, China ' School of Shangmao, Zhejiang Technical Institute of Economics, Hangzhou, Zhejiang, China ' School of Information Science and Technology, Fudan University, Shanghai, China
Abstract: K-Nearest Neighbours (KNN) is one of the fundamental classification methods in machine learning. The performance of KNN method is restricted by the number of neighbours k. It is obvious that the outliers appear when dealing with small data samples. In this paper, we propose a hybrid framework of the feature weighted support vector machine as well as locally weighted k-nearest neighbour (SLKNN) to overcome this problem. In our method, we first use support vector machine to calculate the eigenvector of feature of data, then apply this eigenvector into distance metric as the weight of the feature. Finally, the distance metric is used in locally weighted k-nearest neighbour. The experiments on UCI data sets show that the proposed SLKNN performs better than some KNN-based methods.
Keywords: artificial intelligence; eigenvector; K-nearest neighbours; locally weighted.
International Journal of Wireless and Mobile Computing, 2020 Vol.19 No.3, pp.256 - 266
Received: 06 May 2020
Accepted: 23 Jun 2020
Published online: 30 Oct 2020 *