A Memory-Based Approach to Anti-Spam Filtering for Mailing Lists
Information Retrieval
Ant Colony Optimization
An Ant Colony Optimization Algorithm for Learning Classification Rules
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
Mining software repositories for comprehensible software fault prediction models
Journal of Systems and Software
A fuzzy similarity approach for automated spam filtering
AICCSA '08 Proceedings of the 2008 IEEE/ACS International Conference on Computer Systems and Applications
Data mining with an ant colony optimization algorithm
IEEE Transactions on Evolutionary Computation
Classification With Ant Colony Optimization
IEEE Transactions on Evolutionary Computation
Support vector machines for spam categorization
IEEE Transactions on Neural Networks
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The cost estimates for receiving unsolicited commercial email messages, also known as spam, are threatening. Spam has serious negative impact on the usability of electronic mail and network resources. In addition, it provides a medium for distributing harmful code and/or offensive content. The work in this paper is motivated by the dramatic increase in the volume of spam traffic in recent years and the promising ability of ant colony optimization in data mining. Our goal is to develop an ant-colony based spam filter and to empirically evaluate its effectiveness in predicting spam messages. We also compare its performance to three other popular machine learning techniques: Multi-Layer Perceptron, Naïve Bayes and Ripper classifiers. The preliminary results show that the developed model can be a remarkable alternative tool in filtering spam; yielding better accuracy with considerably smaller rule sets which highlight the important features in identifying the email category.