Effect of feature selection methods on machine learning classifiers for detecting email spams

Abstract

This research presents the effects of using features selected by two feature selection methods i.e. Genetic Search and Greedy Stepwise Search on popular Machine Learning Classifiers like Bayesian, Naive Bayes, Support Vector Machine and Genetic Algorithm. Tests were performed on two different publicly available spam email datasets: Enron and SpamAssassin. Results show that, Greedy Stepwise Search is a good method for feature selection for spam email detection. Among the Machine Learning Classifiers, Support Vector Machine has been found to be the best both in terms of accuracy and False Positive rate