Email Classification Using Data Reduction Method

MD Rafiqul Islam, Yang Xiang

Research output: Book chapter/Published conference paperConference paperpeer-review


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.
Original languageEnglish
Title of host publicationCHINACOM 2010
Subtitle of host publication5th proceedings
Place of PublicationUSA
PublisherInstitute of Electrical and Electronics Engineers
Number of pages5
ISBN (Electronic)9739639799974
Publication statusPublished - 2010
EventInternational Conference on Communications and Networking in China (CHINACOM) - Beijing, China, China
Duration: 25 Aug 201027 Aug 2010


ConferenceInternational Conference on Communications and Networking in China (CHINACOM)


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