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An email classification model based on rough set theory

Zhao, Wenqing and Zhang, Zili 2005, An email classification model based on rough set theory, in Proceedings of the 2005 International Conference on Active Media Technology : (AMT2005) : May 19-21, 2005, Kagawa International Conference Hall, Takamatsu, Kagawa, Japan, IEEE Xplore, Piscataway, N.J., pp. 403-408.

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Title An email classification model based on rough set theory
Author(s) Zhao, Wenqing
Zhang, Zili
Conference name International Conference on Active Media Technology (3rd : 2005 : Kagawa-gun, Japan)
Conference location Kagawa, Japan
Conference dates 19-21 May 2005
Title of proceedings Proceedings of the 2005 International Conference on Active Media Technology : (AMT2005) : May 19-21, 2005, Kagawa International Conference Hall, Takamatsu, Kagawa, Japan
Editor(s) Tarumi, H.
Li, Y.
Yoshida, T.
Publication date 2005
Conference series Active Media Technology Conference
Start page 403
End page 408
Publisher IEEE Xplore
Place of publication Piscataway, N.J.
Summary 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.
Notes This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
ISBN 0780390350
9780780390355
Language eng
Field of Research 080105 Expert Systems
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2005, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30005717

Document type: Conference Paper
Collections: School of Information Technology
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.