Unethical e-mail senders bear little or no cost for mass distribution of messages, yet normal e-mail users are forced to spend time and effort in reading undesirable messages from their mailboxes. Due to the rapid increase of electronic mail (or e-mail), several people and companies found it an easy way to distribute a massive amount of undesired messages to a tremendous number of users at a very low cost. These unwanted bulk messages or junk e-mails are called spam messages .Several machine learning approaches have been applied to this problem. In this paper, we explore a new approach based on Bayesian classification that can automatically classify e-mail messages as spam or legitimate. We study its performance for various datasets.
"IMPROVING SPAM EMAIL FILTERING EFFICIENCY USING BAYESIAN BACKWARD APPROACH PROJECT,"
International Journal of Computer Science and Informatics: Vol. 4
, Article 1.
Available at: https://www.interscience.in/ijcsi/vol4/iss1/1