An Enhanced Genetic Programming Approach for Detecting Unsolicited Emails

Identification of unsolicited emails (spams) is now a well-recognized research area within text classification. A good email classifier is not only evaluated by performance accuracy but also by the false positive rate. This research presents an Enhanced Genetic Programming (EGP) approach which works by building an ensemble of classifiers for detecting spams. The proposed classifier is tested on the most informative features of two public ally available corpuses (Enron and Spam assassin) found using Greedy stepwise search method. Thereafter, the proposed ensemble of classifiers is compared with various Machine Learning Classifiers: Genetic Programming (GP), Bayesian, Naïve Bayes (NB), J48, Random forest (RF), and SVM. Results of this study indicate that the proposed classifier (EGP) is the best classifier among those compared in terms of performance accuracy as well as false positive rate.