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
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;
Conference_Titel :
Asian Language Processing (IALP), 2010 International Conference on
Conference_Location :
Harbin
Print_ISBN :
978-1-4244-9063-9
DOI :
10.1109/IALP.2010.46