Hadoop-based MapReduce Model of Bayesian Filtering
Qinghua Zeng · Jisuanji gongcheng · 2013
There are some disadvantages of mass mail filtering for large mail systems on the traditional distributed system including programming difficulties, low efficiency, mass system and network resources consumed. Taking advantage of the high performance of the cloud computing in processing data processing effectively, a MapReduce model of Bayesian mail filtering based on Hadoop is proposed. It improves the traditional Bayesian filtering algorithms and optimizes the mail training and filtering processes. Experimental results show that, compared with traditional distributed computing model, the Hadoop-based MapReduce model of Bayesian anti-spam mail filtering performs better in recall, precision and accuracy, reduces the cost of mail learning and classifying and improves the system efficiency.