Studying cost-sensitive learning for multi-class imbalance in Internet traffic classification

作     者：LIU Zhen LIU Qiong 

作者机构：School of Soft EngineeringSouth China University of Technology School of Computer Science and EngineeringSouth China University of Technology 

出 版 物：《The Journal of China Universities of Posts and Telecommunications》 (中国邮电高校学报（英文版）)

年 卷 期：2012年第19卷第6期

页      面：63-72页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 081201[工学-计算机系统结构] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Basic Research Program of China(2007CB307100 2007CB307106) 

主　　题：Internet traffic classification minority class cost matrix machine learning 

摘      要：Cost-sensitive learning has been applied to resolve the multi-class imbalance problem in Internet traffic classification and it has achieved considerable results. But the classification performance on the minority classes with a few bytes is still unhopeful because the existing research only focuses on the classes with a large amount of bytes. Therefore, the class-dependent misclassification cost is studied. Firstly, the flow rate based cost matrix （FCM） is investigated. Secondly, a new cost matrix named weighted cost matrix （WCM） is proposed, which calculates a reasonable weight for each cost of FCM by regarding the data imbalance degree and classification accuracy of each class. It is able to further improve the classification performance on the difficult minority class （the class with more flows but worse classification accuracy）. Experimental results on twelve real traffic datasets show that FCM and WCM obtain more than 92% flow g-mean and 80% byte g-mean on average; on the test set collected one year later, WCM outperforms FCM in terms of stability.