• DocumentCode
    2388193
  • Title

    Naïve Bayes Text Classifier

  • Author

    Zhang, Haiyi ; Li, Di

  • Author_Institution
    Acadia Univ., Wolfville
  • fYear
    2007
  • fDate
    2-4 Nov. 2007
  • Firstpage
    708
  • Lastpage
    708
  • Abstract
    Text classification algorithms, such SVM, and Naive Bayes, have been developed to build up search engines and construct spam email filters. As a simple yet powerful sample of Bayesian theorem, naive Bayes shows advantages in text classification yielding satisfactory results. In this paper, a spam email detector is developed using naive Bayes algorithm. We use pre-classified emails (priory knowledge) to train the spam email detector. With the model generated from the training step, the detector is able to decide whether an email is a spam email or an ordinary email.
  • Keywords
    Bayes methods; text analysis; unsolicited e-mail; Bayesian theorem; naive Bayes; search engines; spam email filters; text classification algorithms; Bayesian methods; Classification algorithms; Computer science; Detectors; Inference algorithms; Probability; Search engines; Support vector machine classification; Support vector machines; Text categorization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Granular Computing, 2007. GRC 2007. IEEE International Conference on
  • Conference_Location
    Fremont, CA
  • Print_ISBN
    978-0-7695-3032-1
  • Type

    conf

  • DOI
    10.1109/GrC.2007.40
  • Filename
    4403192