Chinese Spam Filter Based on Relaxed Online Support Vector Machine

Author

Han, Yong ; He, Xiaoning ; Yang, Muyun ; Qi, Haoliang ; Song, Chao

Author_Institution

Sch. of Comput. Sci. & Technol., Harbin Inst. of Technol., Harbin, China

fYear

2010

fDate

28-30 Dec. 2010

Firstpage

185

Lastpage

188

Abstract

Spam filtering is a classical online learning problem. When the size of training sample set becomes larger and larger, the speed of Online SVM is becoming slower and slower. Therefore, we relax the constraints of Online SVM and get the Relaxed Online SVM (ROSVM) model, which can not only improve the speed, but also can ensure the performance. In this paper, we applied this model to Chinese spam filter. Our model outperforms the best system of TREC 2006 Chinese spam filter track. Our filter also participated in the SEWM 2010 spam filter track, and got the best 1-ROCA% of the delayed feedback task and the active learning task.

Keywords

information filtering; support vector machines; unsolicited e-mail; SEWM 2010 spam filter track; TREC 2006 Chinese spam filter track; online learning problem; relaxed online support vector machine; spam filtering; Feature extraction; Filtering; Machine learning algorithms; Support vector machines; Training; Unsolicited electronic mail; Chinese spam filtering; Relaxed online SVM; online learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Asian Language Processing (IALP), 2010 International Conference on

Conference_Location

Harbin

Print_ISBN

978-1-4244-9063-9

Type

conf

DOI

10.1109/IALP.2010.90

Filename

5681610