计算机工程2012，Vol.38Issue(16)：203-206,211,5.DOI:10.3969/j.issn.1000-3428.2012.16.053
基于Map Reduce的Bagging贝叶斯文本分类
Bagging Bayes Text Classification Based on Map Reduce
摘要
Abstract
In order to solve the problem that the classification is difficult on massive text data under the framework of a centralized system, this paper proposes a Bagging Bayes text classification algorithm based on Map Reduce. It introduces the Naive Bayes text classification algorithm. Combined with the Bagging algorithm, it uses Map Reduce parallel programming model to realize the algorithm on Hadoop platform. Experimental results show that this algorithm can be used in the classification of large-scale text data sets, have good accuracy and short running time.关键词
分布式/Map Reduce模型/文本分类/集成学习/朴素贝叶斯/Bagging算法Key words
distribution/ Map Reduce model/ text classification/ ensemble learning/ Naive Bayes/ Bagging algorithm分类
信息技术与安全科学引用本文复制引用
冀素琴,石洪波,卫洁..基于Map Reduce的Bagging贝叶斯文本分类[J].计算机工程,2012,38(16):203-206,211,5.基金项目
国家自然科学基金资助项目(60873100) （60873100）
山西省自然科学基金资助项目(2009011017-4) （2009011017-4）
