关键词: 垃圾邮件, 邮件过滤, 贝叶斯算法, MapReduce, 数据处理

Abstract: The Bayesian anti-spam filter has strong classification capacity and high accuracy, but the mail training and learning at early stage consume mass system and network resources and affect system efficiency. A MapReduce-based Bayesian anti-spam filtering mechanism was proposed, which first improved the traditional Bayesian filtering technique, and then optimized the mail training and learning by taking advantage of mass data processing of MapReduce. The experimental results show that, compared with the traditional Bayesian filtering technique, K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) algorithms, the MapReduce-based Bayesian anti-spam filtering mechanism performs better in recall, precision and accuracy, reduces the cost of mail learning and classifying and improves the system efficiency.

Key words: spam E-mail, E-mail filter, Bayesian algorithm, MapReduce, data processing