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

Abstract

In this paper, we proposed an efficient spamfiltering method based on decision tree data mining technique, analyzed the as-sociation rules about spams, and applied these rules to develop a systematized spamfiltering method. Our method possessedthe following three major superiorities: (i) checking only an e-mail’s header section to avoid the low-operating efficiency inscanning an e-mail’s content. Moreover, the accuracy offiltering was enhanced simultaneously. (ii) In order that the probablemisjudgment in identifying an unknown e-mail could be“reversed”, we had constructed a reversing mechanism to help theclassification of unknown e-mails. Thus, the overall accuracy of ourfiltering method will be increased. (iii) Our method wasequipped with a re-learning mechanism, which utilized the supervised machine learning method to collect and analyze eachmisjudged e-mail. Therefore, the revision information learned from the analysis of misjudged e-mails incrementally gavefeedback to our method, and its ability of identifying spams would be improved.