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

Shugang Liu, Kebin Cui

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.


Full Text: PDF DOI: 10.5539/mas.v3n10p27

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This work is licensed under a Creative Commons Attribution 3.0 License.

Modern Applied Science   ISSN 1913-1844 (Print)   ISSN 1913-1852 (Online)

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