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基于PCA和禁忌搜索的网络流量特征选择算法 被引量：5

Algorithm of Network Traffic Feature Selection Based on PCA and Tabu Search
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摘要 针对网络流量特征属性选择的寻优和效率问题,提出了一种PCA结合禁忌搜索的网络流量特征选择方法。该方法通过PCA对高维特征属性空间进行特征约减,并利用禁忌搜索得到全局最优特征子集。实验证明,相比流行的遗传算法(GA)和粒子群寻优算法(PSO-SVM),PCA和禁忌搜索方法具有更好的处理效率和特征选择精度。 A network traffic feature selection method using principal component analysis and tabu search（PCA-TS） was proposed for the purpose of the efficiency and quality when using lecture selection. This approach reduces high-dimen- sional features using PCA and gets the optimal feature subset on the basis of tabu search. Experiment shows that PCA- TS method has better efficiency and selection accuracy compared with GA and PSO-SVM.
作者 冶晓隆 兰巨龙 郭通
机构地区 国家数字交换系统工程技术研究中心
出处 《计算机科学》 CSCD 北大核心 2014年第1期187-191,共5页 Computer Science
基金 国家科技支撑计划课题(2012BAH02B01 2012BAH02B03) 国家高技术研究发展计划(863计划)课题(2011AA01A103 2011AA01A101) 国家高技术研究发展计划(863计划)基金资助项目(2011BAH19B04)资助
关键词 特征约减 特征选择 主成分分析 禁忌搜索 Feature reduction,Feature selection,Principal component analysis（PCA）,Tabu search
分类号 TP393 [自动化与计算机技术—计算机应用技术]
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参考文献12

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二级参考文献31

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共引文献274

同被引文献48

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计算机科学
2014年 第1期

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