• DocumentCode
    1946518
  • Title

    An email classification model based on rough set theory

  • Author

    Zhao, Wenqing ; Zhang, Zili

  • Author_Institution
    Sch. of Comput. Sci. & Technol., North China Electr. Power Univ., Baoding, China
  • fYear
    2005
  • fDate
    19-21 May 2005
  • Firstpage
    403
  • Lastpage
    408
  • Abstract
    The communication via email is one of the most popular services of the Internet. Emails have brought us great convenience in our daily work and life. However, unsolicited messages or spam, flood our email boxes, which results in bandwidth, time and money wasting. To this end, this paper presents a rough set based model to classify emails into three categories - spam, no-spam and suspicious, rather than two classes (spam and non-spam) in most currently used approaches. By comparing with popular classification methods like Naive Bayes classification, the error ratio that a non-spam is discriminated to spam can be reduced using our proposed model.
  • Keywords
    Bayes methods; Internet; authorisation; pattern classification; rough set theory; unsolicited e-mail; Internet; Naive Bayes classification; email box; email classification model; rough set theory; spam classification; unsolicited messages; Classification tree analysis; Decision trees; Electronic mail; Error analysis; Filtering; Internet; Set theory; Support vector machine classification; Support vector machines; Unsolicited electronic mail;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Active Media Technology, 2005. (AMT 2005). Proceedings of the 2005 International Conference on
  • Print_ISBN
    0-7803-9035-0
  • Type

    conf

  • DOI
    10.1109/AMT.2005.1505383
  • Filename
    1505383