A REVIEW ARTICLE ON NAIVE BAYES CLASSIFIER WITH VARIOUS SMOOTHING TECHNIQUES?

Naive Bayes is very popular in commercial and open-source anti-spam e-mail filters. There are, however, several forms of Naive Bayes, something the anti-spam literature does not always acknowledge. We discuss five different versions of Naive Bayes, and compare them on six new, non-encoded datasets, that contain ham messages of particular Enron users and fresh spam messages. The new datasets, which we make publicly available, are more realistic than previous comparable benchmarks, because they maintain the temporal order of the messages in the two categories, and they emulate the varying proportion of spam and ham messages that users receive over time. In this paper we have discovered various aspects of Naïve Bayes Classifier and smoothing techniques for extraction of useful data along with our research criteria.