Earticle

초록

영어

Content-based email spam filtering is a challenging problem in which emails are often represented as high-dimensional data. This paper proposes an approach to email spam filtering based on max-margin semi-NMF (MNMF). MNMF combines the ideas of semi-NMF and max-margin and performs dimension reduction and classification simultaneously. In MNMF, we employ the same approach as Semi-NMF to update the coefficient matrix (while the other parameters are fixed) instead of quadratic programming. Simulation experiments were performed on two public Chinese email corpuses. The results show that MNMF is much faster and performs much better than support vector machine (SVM) classifiers that use features extracted by principal component analysis or linear discriminant analysis, and the MNMF method also outperforms SVM classification schemes in combination with feature extractions based on NMF and Semi-NMF

목차

Abstract
 1. Introduction
 2. NMF and Semi-NMF
  2.1. NMF
  2.2 Semi-NMF
 3. Main Title
 4. Experiments
  4.1. Data Sets and Evaluation Metrics
  4.2. Designs and Results Analysis
 5. Conclusions and Future Work
 References

키워드

저자

• Zun-xiong Liu [ School of Information Engineering, East China Jiaotong University, Nanchang 330013, China ]
• Shan-shan Tian [ School of Information Engineering, East China Jiaotong University, Nanchang 330013, China ]
• Zhi-qiang Huang [ School of Information Engineering, East China Jiaotong University, Nanchang 330013, China ]
• Jiang-wei Liu [ School of Information Engineering, East China Jiaotong University, Nanchang 330013, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Security and Its Applications

• 간기

  격월간

• pISSN

  1738-9976

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605