Discovering classification rules for email spam filtering with an ant colony optimization algorithm
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Using GMDH-based networks for improved spam detection and email feature analysis
Applied Soft Computing
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E-mail spam has become an epidemic problem that can negatively affect the usability of electronic mail as a communication means. Besides wasting users’ time and effort to scan and delete the massive amount of junk e-mails received; it consumes network bandwidth and storage space, slows down e-mail servers, and provides a medium to distribute harmful and/or offensive content. Several machine learning approaches have been applied to this problem. In this paper, we explore a new approach based on fuzzy similarity that can automatically classify e-mail messages as spam or legitimate. We study its performance for various conjunction and disjunction operators for several datasets. The results are promising as compared with a naïve Bayesian classifier. Classification accuracy above 97% and low false positive rates are achieved in many test cases.