Active Learning Method for Chinese Spam Filtering
Guanglu Sun · International Journal of Performability Engineering · 2017
An active learning method is put forward to filter Chinese spam.In terms of training the filtering model, labeling all of the emails seems to be costly and time-consuming, while unlabeled emails can be easily accessed.Misclassification and a low-certainty method is proposed to reduce the number of labeled emails.The ROSVM model is also utilized as the online filtering model.The experimental results show that the proposed method not only decreases the number of training emails and the computational cost of spam filter, but also improves the accuracy of the filter.