Bayesian Spam Filtering Mechanism Based on Decision Tree of Attribute Set Dependence in the MapReduce Framework

Bayesian spam filtering is a classification method based on the theory of probability and statistics, and the Bayesian spam filtering based on Mapreduce can solve the defect of the traditional Bayesian spam filtering that consumes large amounts of system resources and network resources when the mail set is pre-training. It needs to classify mails manually in the pre-training phase of mail set, which consumes a lot of human and financial resources and affects the efficiency of the system. Bayesian spam filtering mechanism based on decision tree of the attribute sets dependence in the MapReduce framework which is presented in this paper. And the decision tree of attribute sets dependence is used in the training stage of the mail set, which improves execution efficiency of the system by lowering the time complexity.