• 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