SPAM Filtering with Naive Bayes
Haifeng Yang · Harbin Ligong Daxue xuebao · 2014
The effectiveness of Naive Bayes in spam filtering depends on the modelling of the mail contents. However,mail content modelling is not mature,which limits the performance of Bayesian method in spam filtering. This paper presents three kinds of probability distribution to model email content,and proposes three Nave Bayes algorithms based on different probability distributions. To improve training efficiency,the incremental training algorithm is utilized in the experimental procedure. Experiments on trec06p and trec05p- 1 show that the three proposed algorithms can achieve good performance in different sceneries. Such a finding also provides effective basis for the selection of the filtering methods.