Interactive Spam Filtering with Active Learning and Feature Selection

This paper proposes an interactive spam filtering method that utilizes active learning and feature selection. Se-lecting effective features are very important in spam fil-tering because spam mails include so many meaningless words that are slightly different from each other. Thus se-lecting effective and ineffective features is promising ap-proach.Although traditional feature selection methods have been done based on some amount of labeled training data, this assumption does not hold in interactive spam filter-ing. We propose a method to selecting effective features through active learning in spam filtering using naive Bayes approach. Experimental results show that our method out-performs traditional methods that operate with no feature selection. 1