Northern Bald Ibis Algorithm-Based Novel Feature Selection Approach

Abstract

Emails have become one of the popular and flexible web or mobile-based applications that enables users to communicate. For decades, the most severe problem identified in email applications was unwanted emails. Electronic spam is also referred as spam emails, in which unsolicited and unwanted mails are Received. Making an email mailbox clean by detecting and eliminating all the spam mails is a challenging task. Classification-based email filtering is one of the best approaches used by many researchers to deal with the spam email filtering problem. In this work, the NOA optimization algorithm and the SVM classifier are used for getting an optimal feature subset of the Enron-spam dataset and classifying the obtained optimal feature subset. NOA is a recently developed metaheuristic algorithm which is driven by mimicking the energy saving flying pattern of the Northern Bald Ibis (Threskiornithidae). The performance comparisons have been made with other existing methods. The superiority of the proposed novel feature selection approach is evident in the analysis and comparison of the classification results.