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

作　　者：陈念[1,2] 唐振民[2] 

机构地区：[1]池州学院数学与计算机科学系,安徽池州247000 [2]南京理工大学计算机科学与工程学院,南京210094

出　　处：《计算机工程与应用》2014年第22期170-174,共5页Computer Engineering and Applications

基　　金：安徽省教育厅自然重点项目(No.KJ2012A211)

摘　　要：针对垃圾邮件在线过滤的实际应用,在委员会投票算法采样学习的基础上,提出动态提升采样门槛,在无标签样本池中阶梯式获取高信息量训练样本的方法。该方法能够在稳定识别精度的前提下,进一步降低用于标注和学习的样本数量,压缩由此带来的时间成本。通过在UCI的Spambase数据集上仿真,证明了该方法在改善学习效率方面的有效性。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 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]