计算机工程与应用Issue(22)：170-174,5.DOI:10.3778/j.issn.1002-8331.1211-0016
QBC主动采样学习在垃圾邮件在线过滤中的应用
Method of spam filtering online based on QBC active sampling learning algorithm
摘要
Abstract
A method is put forward in the paper which can get informative samples from unlabeled-sample pool with stepped way. The method which is based on query-by-committee algorithm increases the sampling threshold dynamically and it is in order to solve the problem of spam filtering online. Through the new method, the number of samples which is used for labeling and training is further reduced and the accuracy of classifier can remain stable. By experiments on Spam-base datasets, the effectiveness which can improve efficiency of machine learning is certificated.关键词
垃圾邮件过滤/版本空间/主动学习/投票熵/委员会投票算法Key words
spam filtering/version space/active learning/vote entropy/query-by-committee algorithm分类
信息技术与安全科学引用本文复制引用
陈念,唐振民..QBC主动采样学习在垃圾邮件在线过滤中的应用[J].计算机工程与应用,2014,(22):170-174,5.基金项目
安徽省教育厅自然重点项目（No.KJ2012A211）。 （）
