한국컴퓨터정보학회논문지 (Journal of the Korea Society of Computer and Information)

  • 제17권3호
  • /
  • Pages.67-76
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  • 2012
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  • 1598-849X(pISSN)
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  • 2383-9945(eISSN)
한국컴퓨터정보학회 (Korean Society of Computer Information)
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조절할 수 있는 볼록한 덮개 서포트 벡터 머신에 기반을 둔 트래픽 분류 방법

Traffic Classification based on Adjustable Convex-hull Support Vector Machines

  • 위즈빈 (경북대학교 전자전기컴퓨터학부) ;
  • 최용도 (경북대학교 컴퓨터학부) ;
  • 길기범 (경북대학교 전자전기컴퓨터학부) ;
  • 김승호 (경북대학교 컴퓨터학부)
  • Yu, Zhibin (School of Electronic and Computer Science, Kyungpook National University) ;
  • Choi, Yong-Do (School of Computer Science and Engineering, Kyungpook National University) ;
  • Kil, Gi-Beom (School of Electronic and Computer Science, Kyungpook National University) ;
  • Kim, Sung-Ho (School of Computer Science and Engineering, Kyungpook National University)
  • 투고 : 2011.11.22
  • 심사 : 2011.12.28
  • 발행 : 2012.03.30
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초록

트래픽 분류는 트래픽 관리하는데 중요한 역할을 차지하고 있다. 전통적인 방법은 P2P와 암호화 트래픽을 제대로 분류할 수 없는 문제가 있다. 서포트 벡터 머신은 기존의 문제를 해결할 수 있고 병목 현상을 극복할 수 있는 유용한 분류 도구이다. 하지만 서포트 벡터 머신의 주요 장점은 이차 프로그래밍(QP)문제 때문에 큰 데이터 집단을 훈련하는데 시간을 소모한다. 그러나 유용한 서포트 벡터는 전체 데이터에서 극히 일부분이다. 만약 우리가 훈련전에 쓸모없는 벡터들을 삭제할 수 있다면, 시간을 절약하고 정확도를 유지할 수 있다. 이 논문에서 우리는 대규모 데이터를 다룰 때 훈련 속도를 빠르게 하기위해 순차적인 방법을 통해 쓸모없는 벡터들을 제거하기 위한 가능성을 논의하였다.

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.

키워드

참고문헌

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