网络流量特征选择方法中的分治投票策略研究  

The Divide-Conquer and Voting Strategy for Traffic Feature Selection

作　　者：高文[1] 钱亚冠[1,2] 吴春明[1] 郭晔[3] 朱凯[1] 陈双喜[4] 

机构地区：[1]浙江大学计算机学院,浙江杭州310027 [2]浙江科技学院理学院,浙江杭州310023 [3]浙江大学图书与信息中心,浙江杭州310027 [4]嘉兴职业技术学院,浙江嘉兴314036

出　　处：《电子学报》2015年第4期795-799,共5页Acta Electronica Sinica

基　　金：国家973重点基础研究发展计划(No.2012CB315903);浙江省重点科技创新团队(No.2011R50010-21;No.2013TD20);国家自然科学基金(No.61379118);国家科技支撑计划(No.2014BAH24F01);国家863计划(No.2012AA01A507);浙江省网络媒体云处理与分析工程技术中心开放课题(No.2012E10023-14)

摘　　要：特征选择作为机器学习过程中的预处理步骤,是影响分类性能的关键因素.网络流量具有数据量大,特征维度高的特点,如何快速提取特征子集,并提高分类效率对于基于机器学习的流量分类方法具有重要意义.本文提出基于分治与投票策略的特征提取方法,将数据集分裂为多个子集,分别执行特征提取算法,利用投票方法获得最后的特征子集.实验表明可有效提高特征提取的时间效率,同时使分类器取得良好的分类准确率.Feature selection as a substantial preprocess step is a key factor for improvement of classification accuracy. The network traffic is characterized by huge volume and high dimensions. So how to extract the optimal feature subset in short time is practical for traffic classification based on machine learning. A novel method is proposed, which partitions the traffic dataset into several small subsets, and applies special feature selection algorithm to them respectively. Finally, the optimal feature subset is obtained by voting on these alternative feature subsets. The experiment results show that the proposed method has good time efficiency in searching optimal features and helps to improve classification accuracy efficiently.

关 键 词：分治 投票 流量分类 特征选择 

分 类 号：TP393.4[自动化与计算机技术—计算机应用技术]