Combining SVM classifiers for email anti-spam filtering

Authors:
Ángela Blanco;Alba María Ricket;Manuel Martí-Merino
Affiliations:
Universidad Pontificia de Salamanca, Salamanca, Spain;Universidad Pontificia de Salamanca, Salamanca, Spain;Universidad Pontificia de Salamanca, Salamanca, Spain
Venue:
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Year:
2007

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Abstract

Spam, also known as Unsolicited Commercial Email (UCE) is becoming a nightmare for Internet users and providers. Machine learning techniques such as the Support VectorMachines (SVM) have achieved a high accuracy filtering the spam messages. However, a certain amount of legitimate emails are often classified as spam (false positive errors) although this kind of errors are prohibitively expensive. In this paper we address the problem of reducing particularly the false positive errors in anti-spam email filters based on the SVM. To this aim, an ensemble of SVMs that combines multiple dissimilarities is proposed. The experimental results suggest that the new method outperforms classifiers based solely on a single dissimilarity and a widely used combination strategy such as bagging.