Traffic Classification based on Adjustable Convex-hullSupport Vector Machines

Zhibin Yu (위즈빈); 최용도; 길기범; KIM SUNG-HO (김승호)

Traffic Classification based on Adjustable Convex-hullSupport Vector Machines

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2012, 17(3), pp.67-76
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science

Zhibin Yu ¹, 최용도 ², 길기범 ², KIM SUNG-HO ²

¹경북대학교 전자전기컴퓨터학부
²경북대학교

Accredited

ABSTRACT

Traffic classification plays an important role in traffic management. To traditional methods, P2P and encryption traffic may become a problem. Support Vector Machine (SVM) is a useful classification tool which is able to overcome the traditional bottleneck. The main disadvantage of SVM algorithms is that it’s time-consuming to train large data set because of the quadratic programming (QP) problem. However, the useful support vectors are only a small part of the whole data. If we can discard the useless vectors before training, we are able to save time and keep accuracy. In this article, we discussed the feasibility to remove the useless vectors through a sequential method to accelerate training speed when dealing with large scale data.

KEYWORDS

Visualization, Traffic Classification, Pattern recognition

Citation status

* References for papers published after 2023 are currently being built.

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This paper was written with support from the National Research Foundation of Korea.

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