Particle Swarm Optimization-Aided Feature Selection for Spam Email Classification

Using a finite set of features to help determine an email as spam or non-spam is a very popular way. However, in most cases, the feature selection is empirically verified. This paper investigates how particle swarm optimization algorithm can help select features relevant for spam email classification. The experimental results show that the proposed approach selects the most proper discriminative features while eliminating irrelevant ones.