Telecommunications Science ›› 2016, Vol. 32 ›› Issue (5): 105-113.doi: 10.11959/j.issn.1000-0801.2016132

• research and development • Previous Articles     Next Articles

Internet traffic classification using SVM with flexible feature space

Yaguan QIAN1,Xiaohui GUAN2,Bensheng YUN1,Qiong LOU1,Pengfei MA1   

  1. 1 College of Science,Zhejiang University of Science and Technology,Hangzhou 310023,China
    2 Zhejiang University of Water Resources and Electric Power,Hangzhou 310018,China
  • Online:2017-02-22 Published:2017-02-22
  • Supported by:
    The National Natural Science Foundation of China;The National Natural Science Foundation of China;Education Department Foundation of Zhejiang Province

Abstract:

SVM is a typical machine learning algorithm with prefect generalization capacity,which is suitable for the internet traffic classification.At present,there are two approaches,One-Against-All and One-Against-One,proposed for extending SVM to multi-class problem like traffic classification.However,these approaches are both based on a unique feature space.In fact,the separating capacity of a special traffic feature is not similar to different applications.Hence,flexible feature space for extending SVM was proposed,which constructs independent feature space with optimal discriminability for each binary-SVM and trains them under their own feature space.Finally,these trained binary-SVM were ensemble by One-Against-All and One-Against-One approaches.The experiments show that the proposed approach can efficiently improve the precision and callback of the traffic classifier and easily obtain more reasonable optimal separating hyper-plane.

Key words: support vector machine, flexible feature space, traffic classification

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