Spam Filtering Method Based on Learning from Small Samples
Xuegang Hu · Jisuanji gongcheng · 2010
It is difficult to collect sufficient labeled E-mails for training a client spam classifier.Aiming at the problem,this paper proposes a spam filtering method based on learning from small samples,which improves the filtering performance with unlabeled samples.An initial Nave Bayes(NB) classifier is trained with a dataset of labeled E-mails,and unlabeled E-mails are probabilistically labeled with it.A new classifier is trained with all E-mails,and iterates to convergence with EM algorithm.Experimental results prove that,given labeled small training samples with a size of 5 to 20,the performance of spam filtering can be effectively improved.