Research Article
Email classification using data reduction method
@INPROCEEDINGS{10.4108/chinacom.2010.59, author={Rafiqul Islam and Yang Xiang}, title={Email classification using data reduction method}, proceedings={5th International ICST Conference on Communications and Networking in China}, publisher={IEEE}, proceedings_a={CHINACOM}, year={2011}, month={1}, keywords={Accuracy Classification algorithms Electronic mail Feature extraction Filtering Support vector machines Training}, doi={10.4108/chinacom.2010.59} }
- Rafiqul Islam
Yang Xiang
Year: 2011
Email classification using data reduction method
CHINACOM
ICST
DOI: 10.4108/chinacom.2010.59
Abstract
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.