计算机工程Issue(11)：57-60,64,5.DOI:10.3969/j.issn.1000-3428.2013.11.012
基于Hadoop的贝叶斯过滤MapReduce模型
Hadoop-based MapReduce Model of Bayesian Filtering
摘要
Abstract
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.关键词
云计算/MapReduce模型/Hadoop架构/贝叶斯算法/垃圾邮件/反垃圾邮件过滤Key words
cloud computing/MapReduce model/Hadoop framework/Bayesian algorithm/spam mail/anti-spam mail filtering分类
信息技术与安全科学引用本文复制引用
曾青华,袁家斌,张云洲..基于Hadoop的贝叶斯过滤MapReduce模型[J].计算机工程,2013,(11):57-60,64,5.基金项目
国家“863”计划基金资助项目(2009AA044601)；国家自然科学基金资助重点项目(61139002)；南京航空航天大学基本科研业务费专项基金资助项目(NS2010230) （2009AA044601）
