计算机工程与应用Issue(17)：82-84,163,4.DOI:10.3778/j.issn.1002-8331.1112-0243
一种多分类器联合的集成网络流量分类方法
Network traffic classification based on combination of multi-classifiers
摘要
Abstract
Traditionally, in the area of the network traffic classification, there exists a problem that single learning algorithm lacks classification accuracy and is incapable of adapting to the dynamic network environment. Accordingly, it proposes a novel classification approach which is a combination of multi-classifier. This method combines the features of a range of classifiers and then achieves traffic classification by means of majority voting and instance selection. Moreover, comparative experiments show that this method improves the classification accuracy, the generalization performance and the ability to adapt to the dynamic network environment. However, it is worth noting that the method has a larger implement complexity and time complexity than these of single algorithm.关键词
流量分类/支持向量机/C4.5决策树/贝叶斯网/集成学习Key words
traffic classification/Support Vector Machine(SVM)/C4.5 decision tree/Bayesian Net(BN)/ensemble learning分类
信息技术与安全科学引用本文复制引用
孔蓓蓓,唐学文,汪为汉..一种多分类器联合的集成网络流量分类方法[J].计算机工程与应用,2013,(17):82-84,163,4.基金项目
国家自然科学基金（No.71102065）。 （）
