A spam filtering method based on active Bayesian classification technology
Xuegang Hu · Journal of Hefei University of Technology · 2008
Current estimates indicate that nearly sixty percent of email traffic is regarded as spam and there is little reason to expect this to continue.Machine learning,text categorization and information filter can be effectively used to solve the problem.The proposed state-of the-art classification methods often label their classes firstly when there are a large number of unlabeled emails,which brings up heavy overhead of time and decreases the classification accuracy.Therefore,an active Bayesian classification technology RANB is proposed in this paper,which is used to label the classes of the unlabeled training emails as pretreatment.The experimental study shows that under the conditions of ensuring the capability of the filter in comparison with the classical methods,the method could effectively improve the quality of training samples and has better performance according to the appraisal standard.