An intelligent three-phase spam filtering method based on decision tree data mining

Abstract

In this paper, we proposed an efficient spam filtering method based on decision tree data mining technique, analyzed the association rules about spams, and applied these rules to develop a systematized spam filtering method. Our method possessed the following three major superiorities: (i) checking only an e-mail`s header section to avoid the low-operating efficiency in scanning an e-mail`s content. Moreover, the accuracy of filtering was enhanced simultaneously. (ii) In order that the probable misjudgment in identifying an unknown e-mail could be “reversed”, we had constructed a reversing mechanism to help the classification of unknown e-mails. Thus, the overall accuracy of our filtering method will be increased. (iii) Our method was equipped with a re-learning mechanism, which utilized the supervised machine learning method to collect and analyze each misjudged e-mail. Therefore, the revision information learned from the analysis of misjudged e-mails incrementally gave feedback to our method, and its ability of identifying spams would be improved.