Applications of Support Vector Machine Based on Boolean Kernel to Spam Filtering

Shugang Liu and Kebin Cui

Modern Applied Science, 2009, vol. 3, issue 10, 27

Abstract: Spam is so widely speared that has a bad effect on daily use of E-mail. Nowadays, among the primary technologies of spam filtering, support vector machine (SVM) is applied widely, because it is efficient and has high separating accuracy. The main problem of support vector machine arithmetic is how to choose the kernel function. To solve this problem people propose spam filtering arithmetic of support vector machine based on Boolean kernel. The arithmetic uses filtering methods based on attributes, such as IP address, subject words, keywords in content, enclosure information, etc. These attributes compose the feature vectors, and the vectors are classified by SVM-MDNF based on Boolean kernel. The experiment results show that this arithmetic has high separating accuracy, high recall ratio and precision ratio. The arithmetic has its value in theory and application.

Date: 2009
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://ccsenet.org/journal/index.php/mas/article/download/3936/3463 (application/pdf)
https://ccsenet.org/journal/index.php/mas/article/view/3936 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ibn:masjnl:v:3:y:2009:i:10:p:27

Access Statistics for this article

More articles in Modern Applied Science from Canadian Center of Science and Education Contact information at EDIRC.
Bibliographic data for series maintained by Canadian Center of Science and Education ().