Spam mail filtering system using semantic enrichment

Abstract

As the Internet infrastructure has been developed, E-mail is regrarded as one of the most important methods for exchanging information because of easy usage and low cost. Meanwhile, exponentially growing unwanted mails in users' mailbox have been raised as a main problem. To solve this problem. researchers have suggested many methodologies that are based on Bayesian classification. The kind of system usually shows high performances of precision and recall. But they have several problems. First, it has a cold start problem, that is, training phase has to be done before execution of the system. The system must be trained about spam and non-spam mail. Second. its cost for filtering spam mail is higher than rule-based system. Third. if E-mail has only few terms those represent its contents, the filtering performance is fallen. In this paper, we have focused on the last issued problem and we suggest spam mail filtering system using Semantic Enrichment. For the experiment, we tested the performance by using the measurements like precision, recall, and F1-measure. As compared with Bayesian classifier, the proposed system obtained 4-1%. 10.5% and 7.64% of improved precision, recall and F1-measure, respectively.