Detecting spam email by radial basis function networks

Article type: Research Article

Authors: Jiang, Eric

Affiliations: University of San Diego, 5998 Alcala Park, Serra Hall 150, San Diego, CA 92110, USA. Tel.: +1 619 260 5958; E-mail: jiang@sandiego.edu

Abstract: Over the years, various spam email filtering technology and anti-spam software products have been developed and deployed. Some are designed to stop spam email at the server level, and others apply machine learning algorithms at the client level to identify spam email based on message content. In this paper, a new spam filtering model, RBF-SF, is proposed that detects and classifies email messages by a radial basis function (RBF) network. The model utilizes the valuable email discriminative information from training data and can incorporate additional background email in its learning process. The empirical results of RBF-SF on two benchmark spam testing corpora and a performance comparison with several other popular text classifiers have shown that the model is capable of filtering spam email effectively.

Keywords: Neural networks, radial basis function, text classification, clustering, information retrieval

DOI: 10.3233/KES-2007-11606

Journal: International Journal of Knowledge-based and Intelligent Engineering Systems, vol. 11, no. 6, pp. 409-418, 2007