DocumentCode
1955449
Title
Comparison between Typical Discriminative Learning Model and Generative Model in Chinese Short Messages Service Spam Filtering
Author
Zheng, Xiaoxia ; Liu, Chao ; Huang, Chengzhe ; Zou, Yu ; Yu, Hongwei
Author_Institution
Comput. Sci. & Technol. Dept., Heilongjiang Inst. of Technol., Harbin, China
fYear
2010
fDate
28-30 Dec. 2010
Firstpage
182
Lastpage
184
Abstract
We used the experience of spam filtering on account of Chinese short messages service spam filtering and compared the performances of typical discriminative learning model and generative model, namely naive bayesian model and logistic regression model. Overall, in Chinese short messages service spam filtering, the performance of naive bayesian model is better than logistic regression model using 1-ROCA as evaluating indicator while the final performance of logistic regression model is better than naive bayesian model with the increase in amount of short messages, which is deferent from spam filtering as shown in this experimental results.
Keywords
belief networks; filtering theory; message passing; regression analysis; unsolicited e-mail; 1-ROCA; Bayesian model; Chinese short message service; discriminative model; generative model; logistic regression model; spam filtering; Bayesian methods; Biological system modeling; Feature extraction; Filtering; Logistics; Training; Unsolicited electronic mail; Chinese short messages service spam filtering; N-gram; bayesian model; logistic regression model;
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.2010.46
Filename
5681609
Link To Document