Detecting Spam Email With Machine Learning Optimized With Bio-Inspired Metaheuristic Algorithms

Abstract

Electronic mail has eased communication methods for many organizations as well as individuals. Spammers use this strategy to make fraudulent gains by sending unsolicited emails. This project aims to present a method for detection of spam emails with machine learning algorithms that are optimized with bio-inspired methods. A literature review is carried to explore the efficient methods applied on different datasets to achieve good results. The bio-inspired algorithms like Particle Swarm Optimization and Genetic Algorithm were implemented to optimize the performance of classifiers. Multinomial Naïve Bayes with Genetic Algorithm performed the best overall. The comparison of our results with other machine learning and bio-inspired models to show the best suitable model is also discussed.