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Email categorization using multi-stage classification technique

Islam, Md. Rafiqul and Zhou, Wanlei 2007, Email categorization using multi-stage classification technique, in PDCAT 2007 : Eighth International Conference on Parallel and Distributed Computing, Applications and Technologies : 3-6 December, 2007, Adelaide, Australia, IEEE Computer Society, Los Alamitos, Calif., pp. 51-58.

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Title Email categorization using multi-stage classification technique
Author(s) Islam, Md. Rafiqul
Zhou, Wanlei
Conference name International Conference on Parallel and Distributed Computing, Applications and Technologies (8th : 2007 : Adelaide, Australia)
Conference location Adelaide, Australia
Conference dates 3-6 December 2007
Title of proceedings PDCAT 2007 : Eighth International Conference on Parallel and Distributed Computing, Applications and Technologies : 3-6 December, 2007, Adelaide, Australia
Editor(s) Munro, David S.
Shen, Hong
Sheng, Quan Z.
Detmold, Henry
Falkner, Katrina E.
Izu, Cruz
Coddington, Paul D.
Alexander, Bradley
Zheng, Si-Qing
Publication date 2007
Conference series Parallel and Distributed Computing Applications and Technologies Conference
Start page 51
End page 58
Publisher IEEE Computer Society
Place of publication Los Alamitos, Calif.
Keyword(s) email
false positive
grey list
Summary This paper presents an innovative email categorization using a serialized multi-stage classification ensembles technique. Many approaches are used in practice for email categorization to control the menace of spam emails in different ways. Content-based email categorization employs filtering techniques using classification algorithms to learn to predict spam e-mails given a corpus of training e-mails. This process achieves a substantial performance with some amount of FP tradeoffs. It has been studied and investigated with different classification algorithms and found that the outputs of the classifiers vary from one classifier to another with same email corpora. In this paper we have proposed a multi-stage classification technique using different popular learning algorithms with an analyser which reduces the FP (false positive) problems substantially and increases classification accuracy compared to similar existing techniques.
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 0769530494
9780769530499
Language eng
Field of Research 080499 Data Format not elsewhere classified
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2007, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30008148

Document type: Conference Paper
Collections: School of Engineering and Information Technology
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