Web Service-Enabled Spam Filtering with Naïve Bayes Classification
Wanqing You, Kai Guo Qian, Dan Chia-Tien Lo, Prabir Bhattacharya, Minzhe Guo, Ying Qian · 2015
Electronic mail has nowadays become a convenient and inexpensive way for communication regardless of the distance. However, an increasing volume of unsolicited emails is bringing down the productivity dramatically. There is a need for reliable anti-spam filters to separate such messages from legitimate ones. The Naïve Bayesian classifier is suggested as an effective engine to pick out spam emails. We have developed an anti-spam filter that employs this content-based classifier. This statistic-based classifier was trained on Enron Spam Dataset, a well-known spam/legitimate email dataset. We developed this filter as a Web Service, which would consume the emails user uploads and give back the predicted probability that in what degree the given email is spam. This engine was achieved by Rest easy technology, and consists three phases to train pre-labeled emails and then apply Naïve Bays theorem to calculate email's Spamicity.