Traffic Classification Using Cost Based Decision Tree
A novel method for achieving practical real-time traffic classification is proposed in this paper, which is based on C4.5 decision tree. Most existing traffic classification algorithms only focus on accuracy of the classification results, but lack of considering the various costs in actual deployment. So they cannot guarantee that the obtained tree construction is optimal for hardware and software processing. To solve this problem, our Cost Based Feature Evaluation procedure defines VnitGainRatio as the metric of attributes to find the best tree construction when considering the attribute acquisition and processing cost We also introduce another method called Fuzzy Delicacy Node Selection procedure to choose the more suitable node, when their VnitGainRatio are too close to each other. The experiment results show that the proposed method reduces the average cost compared with similar algorithm.
Computer networks traffic classification decision tree machine learning
Lin Wang Xuan Zhou Rentao Gu
School of Information and Communications Beijing University of Posts and Telecommunications(BUPT) Be Key Laboratory of Network System Architecture and Convergence (Beijing) School of Information and Co
国际会议
哈尔滨
英文
2545-2550
2011-12-24（万方平台首次上网日期，不代表论文的发表时间）