Interactive Spam Filtering with Active Learning and Feature Selection

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