Title :
The Comparison of Chinese Spam Filter Based on Generative Model and Discriminative Model
Author :
Han, Yong ; Wang, Yingying ; Ding, Huafu ; Qi, Haoliang
Author_Institution :
Comput. Sci. & Technol. Dept., Heilongjiang Inst. of Technol., Harbin, China
Abstract :
Previous studies have shown that discriminative model is better than generative model for spam filtering, which is tested on the English dataset. But the study on Chinese Spam Filter is rare. So we compared the performance of Bogo: a classical generative model, Logistic Regression (LR) and Relaxed Online SVM (ROSVM): two typical discriminative models on the Chinese dataset. Bogo system adopts a generative model, which is based on Bayesian algorithm. We choose the public Chinese datasets: TREC06c, SEWM 2008, SEWM 2010, SEWM 2011, as the test dataset with immediate feedback. The discriminative model gives the better results than the generative model based on spam filter. ROSVM gives the best performance on Chinese spam filter.
Keywords :
belief networks; information filters; natural language processing; regression analysis; support vector machines; unsolicited e-mail; Bayesian algorithm; Bogo system; Chinese spam filter; English dataset; ROSVM; classical generative model; discriminative model; logistic regression; public Chinese dataset; relaxed online SVM; Filtering; Logistics; Machine learning; Support vector machines; Training; Unsolicited electronic mail; Bogo; Chinese spam filter; LR; ROSVM; discriminative model; generative model;
Conference_Titel :
Asian Language Processing (IALP), 2011 International Conference on
Conference_Location :
Penang
Print_ISBN :
978-1-4577-1733-8
DOI :
10.1109/IALP.2011.64