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conference contribution
posted on 2010-01-01, 00:00authored byR Islam, Yang Xiang
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.
History
Event
Communications and Networking in China. Conference (5th : 2010 : Beijing, China)
Pagination
1 - 5
Publisher
IEEE
Location
Beijing, China
Place of publication
Piscataway, N.J.
Start date
2010-08-25
End date
2010-08-27
ISBN-13
9780984589333
Language
eng
Publication classification
E1 Full written paper - refereed
Copyright notice
2010, IEEE
Title of proceedings
CHINACOM 2010 : Proceedings of the 5th International ICST Conference on Communications and Networking in China