Statistical-Based Bayesian Algorithm for Effective Email Classification

Xianghui Zhao, Yangping Zhang, Junkai Yi
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引用次数: 1

Abstract

Email is an incontestable communication mode in both professional and personal correspondences. The survey shows that an ordinary white-collar worker spend at least an hour every day to deal with the email. Handling spam which is disguised as normal email is waste our time. In this paper, we propose a spam detection method upon statistical-based Bayesian algorithm. Firstly, the method use actual priori probability of spam instead of constant probability. Secondly, the selective range and rules of tokens is improved. Finally, our method add URLs and images into detection content. The experiment result shows that the improved statistical-based Bayesian classification algorithm works well in practice.
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基于统计的有效邮件分类贝叶斯算法
电子邮件在专业和个人通信中都是无可争议的通信方式。调查显示,普通白领每天至少要花一个小时来处理电子邮件。处理伪装成普通电子邮件的垃圾邮件是浪费我们的时间。本文提出了一种基于统计贝叶斯算法的垃圾邮件检测方法。首先,该方法使用垃圾邮件的实际先验概率,而不是常数概率。其次,改进了令牌的选择范围和规则;最后,我们的方法将url和图像添加到检测内容中。实验结果表明,改进的基于统计的贝叶斯分类算法在实际应用中效果良好。
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