An Approach to Spam Detection by Naive Bayes Ensemble Based on Decision Induction

Spam has been a serious problem to global email users. In this paper, a two-layered spam detection flow was used, which showed the trade-off between accuracy and efficiency. Then we discussed naive Bayes classifiers ensemble based on bagging. By casting spam detection in a decision theoretic framework, a naive Bayes bagging spam detection model based on embedded decision tree is proposed. Then this model was reduced by strict likelihood score bound limitation of the naive Bayes classifiers. Finally, an improved method based on classifier error weighted is presented. The experiment results show that the modification is effective