A Novel Feature Selection Method Based on Principal Component Analysis for Network Traffic Classification
Hongzhi Wang, Yanping Zhang, Zunyi Shang · Advanced materials research · 2014
In network traffic classification, by conventional PCA method, more features still exist due to uniform contribution rates for most of features. To overcome this problem, in this paper, a novel feature selection method is proposed to reduce data dimension of network traffic. A contribution rate of various features in each component is calculated by a new weight criterion. A maxima-order principle is proposed to determine feature selection. Based on three multi-class classification methods, performance comparison is conducted by actual traffic data with 10-fold cross-validation. Experiment shows that the proposed method has higher classification accuracy than conventional PCA method.