Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Communications of the ACM
A Memory-Based Approach to Anti-Spam Filtering for Mailing Lists
Information Retrieval
An evaluation of statistical spam filtering techniques
ACM Transactions on Asian Language Information Processing (TALIP)
A comparison of event models for Naive Bayes anti-spam e-mail filtering
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
Searching for interacting features
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
A comparative performance study of feature selection methods for the anti-spam filtering domain
ICDM'06 Proceedings of the 6th Industrial Conference on Data Mining conference on Advances in Data Mining: applications in Medicine, Web Mining, Marketing, Image and Signal Mining
Support vector machines for spam categorization
IEEE Transactions on Neural Networks
Computing a Comprehensible Model for Spam Filtering
DS '09 Proceedings of the 12th International Conference on Discovery Science
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In this paper, we introduce a novel feature selection method--INTERACT to select relevant words of emails for spam email filtering, i.e. classifying an email as spam or legitimate. Four traditional feature selection methods in text categorization domain, Information Gain, Gain Ratio, Chi Squared, and ReliefF, are also used for performance comparison. Three classifiers, Support Vector Machine (SVM), Naïve Bayes and a novel classifier--Locally Weighted learning with Naïve Bayes (LWNB) are discussed in this paper. Four popular datasets are employed as the benchmark corpora in our experiments to examine the capabilities of these five feature selection methods and the three classifiers. In our simulations, we discover that the LWNB improves the Naïve Bayes and gain higher prediction results by learning local models, and its performance is sometimes better than that of the SVM. Our study also shows the INTERACT can result in better performances of classifiers than the other four traditional methods for the spam email filtering.