Online Spam Filtering Based on Ensemble Learning of Multi-filter
Ting Wang · Zhongwen xinxi xuebao · 2008
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.