A locally weighted KNN algorithm based on eigenvector of SVM Online publication date: Fri, 13-Nov-2020
by Yonghua Wang; Jingyi Lu; Kaidi Zhao
International Journal of Wireless and Mobile Computing (IJWMC), Vol. 19, No. 3, 2020
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.
Online publication date: Fri, 13-Nov-2020
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Wireless and Mobile Computing (IJWMC):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email email@example.com