Discovering classification rules for email spam filtering with an ant colony optimization algorithm

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, aïve 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.