A Comparative Study of Spam Filters : Bayesian to Fuzzy Similarity Approach

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 are doing a comprehensive survey of various spam filters like Naïve Bayesian classifier, Improved Bayesian filter based on SEDA algorithm, Adaptive filter and fuzzy filter. We studied their performance for various performance evaluation metrics for the dataset we have taken. Classification accuracy is 96% and low false positive rates have been achieved in fuzzy filter using bounded-difference method