Earticle

초록

영어

The high false alarm rate appears during the traditional spam recognition method processing the large-scale unbalanced data. A method which transforms the unbalanced issue into the balanced issue is proposed, when the K-means clustering algorithm is improved based on the support vector machine classification model, to obtain the balanced training set. Firstly, the improved K-means clustering algorithm clusters spam and extracts the typical spam，then the training set consists of the typical spam and legitimate messages, and finally the goal of the filtration of spam is realized by trained SVM classification model. Comparing the K-SVM filtration method to standard SVM method through the experiment, the result indicates that the K-SVM filtration method in large-scale unbalance data set can obtain high classified efficiency and the generalization performance.

목차

Abstract
 1. Introduction
 2. Spam Filtering Model based on Statistics
 3. The Improvement of K-means Clustering Algorithm 
 4. The K-SVM Non-equilibrium Spam Filtering Method 
 5. Experiment Result Analysis
 6. Conclusion
 Acknowledgements
 References

키워드

저자

• Zhiqiang Ma [ College of Information Engineering, Inner Mongolia University of Technology, Hohhot, 010080, China ]
• Rui Yan [ College of Information Engineering, Inner Mongolia University of Technology, Hohhot, 010080, China ]
• Donghong Yuan [ College of Information Engineering, Inner Mongolia University of Technology, Hohhot, 010080, China ]
• Limin Liu [ College of Information Engineering, Inner Mongolia University of Technology, Hohhot, 010080, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605