A modified content-based evolutionary approach to identify unsolicited emails

Abstract

This computational research seeks to classify unsolicited versus legitimate emails. A modified version of an existing genetic programming (GP) classifier—i.e., modified genetic programming (MGP)—is implemented to build an ensemble of classifiers to identify unsolicited emails. The proposed classifier is assessed using informative features extracted from two corpora (Enron and SpamAssassin) with the help of the greedy stepwise feature search method. Further, a comparative study is performed with other popular classifiers, such as Bayesian network, naive Bayes, decision tree, random forest (RF), support vector machine (SVM), and GP. Further the results are validated with 20-fold cross-validation and paired T test. The results prove that the proposed classifier performs better in terms of accuracy and false-positive detection in comparison with the other machine learning classifiers tested in this study. Using different training and testing a set of email files from the Enron corpus, ensemble-based classifiers, such as boosted SVM, boosted Bayesian, boosted naive Bayesian, RF, and the proposed MGP classifier, are tested and compared on all metrics, including training and testing time. The findings suggest that the MGP classifier with the greedy stepwise feature search method offers an improvement over alternative methods in detecting unsolicited emails.