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Active Learning Method for Chinese Spam Filtering

Volume 13, Number 4, July 2017 - Paper 18 - pp. 511-518
DOI: 10.23940/ijpe.17.04.p18.511518

Guanglu Suna,b, Shaobo Lia, Teng Chena, Xuhang Lia, Suxia Zhua,b

aSchool of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China
bResearch Center of Information Security & Intelligent Technology, Harbin University of Science and Technology, Harbin, 150080, China

(Submitted on February 20, 2017; Revised on May 11, 2017; Accepted on June 15, 2017)

Abstract:

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.

 

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