Interaction between feature subset selection techniques and machine learning classifiers for detecting unsolicited emails

Abstract

Detection of the spam emails within a set of email files has become challenging task for researchers. Identification of an effective classifier is based not only on high accuracy of detection but also on low false alarm rates, and the need to use as few features as possible. In view of these challenges, this research examines the effects of using features selected by four feature subset selection methods (i.e. Genetic, Greedy Stepwise, Best First, and Rank Search) on popular Machine Learning Classifiers like Bayesian, Naive Bayes, Support Vector Machine, Genetic Algorithm, J48 and Random Forest. Tests were performed on three different publicly available spam email datasets: "Enron", "SpamAssassin" and "LingSpam". Results show that, Greedy Stepwise Search method is a good method for feature subset selection for spam email detection. Among the Machine Learning Classifiers, Support Vector Machine has been found to be the best classifier both in terms of accuracy and False Positive rate. However, results of Random Forest were very close to that of Support Vector Machine. The Genetic classifier was identified as a weak classifier.