基于多过滤器集成学习的在线垃圾邮件过滤

垃圾邮件过滤就是在线对邮件做出Spam(垃圾)或Ham(非垃圾)的判断,这是一种根据客户反馈不断自学习的过程。本文抽取邮件的语言特征和行为特征构建多个简单过滤器,然后采用集成学习方法组合这些简单过滤器,获得了比简单过滤器更高的性能。实验表明单一特征学习的计算复杂性低、速度较快,而集成学习的效果更好。本文提出的将SVM集成学习用于邮件过滤的方法,在各种集成学习方法中效果最好。

Online Spam Filtering Based on Ensemble Learning of Multi-filter

Spam filtering is defined as a task trying to label Emails with Spam or Ham in an online situation,which is essentially a self-learning procedure with user's feedback.There are already some simple filters applying the linguistic features or behavior features.In this paper,we use the ensemble learning method to combine multi-filter and achieve a higher performance than the single one could.The experiment result shows the single feature learning is fast and the ensemble learning has better effects,in which the proposed SVM ensemble method has the highest performance.