关键词: 支持向量机, 词序列核, 相关度量, 邮件过滤

Abstract: The structure of the text is neglected by using the majority of used kernels to classification, so that a lot of semantic information is lost. In order to solve this problem, a Word Sequence Kernel (WSK) based on dependence measure was proposed and used in the field of spam filtering in this paper. Firstly, the features of each E-mail were extracted and the dependence measure of each feature was calculated; then the word sequence kernel was used as kernel function to train Support Vector Machine (SVM), and the decay factor of each feature was calculated by taking the dependence measure of each feature into account in the training process; finally, the optimized SVM filter was used to spam filtering. The experimental results show that the improved word sequence kernel gets higher accuracy compared to the commonly used kernels and string subsequence kernel. The proposed method improves the accuracy of spam filtering.

Key words: Support Vector Machine (SVM), Word-Sequence Kernel (WSK), dependence measure, spam filtering