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.
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.
Field of Research
080303 Computer System Security
Socio Economic Objective
890206 Internet Hosting Services (incl. Application Hosting Services)
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