Adaptive spam filterings system using complement naive bayes model

Abstract

Naïve bayes filter is a simple probabilistic filtering method based on Bayes theorem. A crucial problem with the conventional naïve bayes filter is the assumption of uniform priors in the computation of the posterior distribution. For online data such as email environment where the training data are constantly updated so as to outsmart the tricks of spammers, the prior knowledge cannot be uniform. Skewedness in the prior knowledge caused by the updated information has been reported to affect the accuracy and then the effectiveness of the traditional naïve bayes filter. In this study, the skewedness is addressed using complement naïve bayes model. The complement naïve bayes model was implemented and tested on benchmarked data and the result compared with the results obtained with the results obtained from the conventional naïve bayes filter on the same dataset. The complement naïve bayes based filter outperforms the conventional naïve bayes filter by 5.39%.

Keywords: Spam, Spam filtering, complement naïve bayes, adaptive filtering, prior, bias, accuracy, filter, adaptive, skewedness

Vol. 26, No 1, June, 2019