中文摘要：

针对多分类器集成方法产生的流量分类器在泛化能力方面的局限性,提出一种选择性集成网络流量分类框架,以满足流量分类对分类器高效的需求。基于此框架,提出一种多分类器选择性集成的网络流量分类方法 MCSE（Multiple Classifiers Selective Ensemble network traffic classification method）,解决多分类器的选取问题。该方法首先利用半监督学习技术提升基分类器的精度,然后改进不一致性度量方法对分类器差异性的度量策略,降低多分类器集成方法实现网络流量分类的复杂性,有效减少选择最优分类器的计算开销。实验表明,与Bagging算法和GASEN算法相比,MCSE方法能更充分利用基分类器间的互补性,具有更高效的流量分类性能。

英文摘要：

Aiming at the limitation of traffic classifiers generated with multiple classifiers ensemble in generalisation ability, we put forward a selective ensemble network traffic classification framework for meeting the demand of traffic classification in classifiers efficiency. Based on this framework, we propose a kind of multiple classifiers selective ensemble network traffic classification method （ MCSE ） to solve the multiple classifiers selection issue. The method increases the classifiers＇ accuracy by utilising semi-supervised learning technology first, and then improves the measuring strategy of inconsistency metric method on difference of classifiers, decreases the complexity in the implementation of network traffic classification with multiple classifier ensemble method, effectively reduces the computing cost on selecting optimal classifier. Experimental results show that the MCSE method can take full advantage of the complementarily between the base classifiers and has better efficiency in traffic classification performance than Bagging and GASEN algorithms.