Abstract
The difficulties of email spam detection system associated with high dimensionality in feature selection process and low accuracy of spam email classification. However, in machine learning, Feature selection (FS) as a global optimization problem decreases irrelevant and redundant data and creates a set of acceptable results with high accuracy. This paper presents a feature selection algorithm based on particle swarm optimization (PSO), which decreases dimensionality and improves the accuracy of spam email classification. PSO as a computational model fallows the social behavior of bird flocking or fish schooling. The proposed PSO-based feature selection algorithm searches the feature space for the best feature subsets. The evolution of feature selected is determined by a fitness function. The classifier performance and the length of selected feature vector as a classifier input are considered for performance evaluation using Ling-Spam and SpamAssassin databases. Experimental results show that the PSO-based feature selection algorithm was presented to generate excellent feature selection results with the minimal set of selected features to be caused by a high accuracy of spam email classification based on Multi-Layer Perceptron (MLP) classifier.
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Behjat, A.R., Mustapha, A., Nezamabadi-pour, H., Sulaiman, M.N., Mustapha, N. (2013). A PSO-Based Feature Subset Selection for Application of Spam /Non-spam Detection. In: Noah, S.A., et al. Soft Computing Applications and Intelligent Systems. M-CAIT 2013. Communications in Computer and Information Science, vol 378. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40567-9_16
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DOI: https://doi.org/10.1007/978-3-642-40567-9_16
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