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Email classification using data reduction method

Islam, Rafiqul and Xiang, Yang 2010, Email classification using data reduction method, in CHINACOM 2010 : Proceedings of the 5th International ICST Conference on Communications and Networking in China, IEEE, Piscataway, N.J., pp. 1-5.

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Title Email classification using data reduction method
Author(s) Islam, Rafiqul
Xiang, Yang
Conference name Communications and Networking in China. Conference (5th : 2010 : Beijing, China)
Conference location Beijing, China
Conference dates 25-27 Aug. 2010
Title of proceedings CHINACOM 2010 : Proceedings of the 5th International ICST Conference on Communications and Networking in China
Editor(s) [Unknown]
Publication date 2010
Conference series Communications and Networking in China Conference
Start page 1
End page 5
Total pages 5
Publisher IEEE
Place of publication Piscataway, N.J.
Summary Classifying user emails correctly from penetration of spam is an important research issue for anti-spam researchers. This paper has presented an effective and efficient email classification technique based on data filtering method. In our testing we have introduced an innovative filtering technique using instance selection method (ISM) to reduce the pointless data instances from training model and then classify the test data. The objective of ISM is to identify which instances (examples, patterns) in email corpora should be selected as representatives of the entire dataset, without significant loss of information. We have used WEKA interface in our integrated classification model and tested diverse classification algorithms. Our empirical studies show significant performance in terms of classification accuracy with reduction of false positive instances.
ISBN 9780984589333
Language eng
Field of Research 080303 Computer System Security
Socio Economic Objective 890206 Internet Hosting Services (incl. Application Hosting Services)
HERDC Research category E1 Full written paper - refereed
HERDC collection year 2010
Copyright notice ©2010, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30034380

Document type: Conference Paper
Collection: School of Information Technology
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