An Enhanced Particle Swarm Optimization algorithm for E-mail Spam Filtering

Abstract

The current challenges experienced in spam email detection systems is directly associated with the low accuracy of spam email classification and high dimensionality in feature selection processes. However, Feature selection (FS) as a global optimization approach in machine learning decreases data redundancy and creates a set of accurate and acceptable results. In this paper, a particle swarm optimization (PSO) algorithm is enhanced by using a logistic chaotic map for decreasing the dimensionality of features and enhance the accuracy of classifying spam emails. The features are represented in a binary from for each particle; in other words, the features are converted to binary using a sigmoid function. The selection of the features is based on a fitness function which is dependent on the achieved accuracy using Support Vector Machine (SVM). The performance of the classifier and the dimension of the selected features vector as a classifier input are considered when evaluating the performance of the Chaotic Binary PSO (CBPSO) using SpamBase dataset. The outcome of the experiments showed the BPSO to achieve good FS results even with a small set of selected features.