International Workshop on Multimedia Content Representation, Classification and Security, MRCS 2006, İstanbul, Turkey, 11 - 13 September 2006, vol.4105 LNCS, pp.635-642
Electronic mail is an important communication method for most computer users. Spam e-mails however consume bandwidth resource, fill-up server storage and are also a waste of time to tackle. The general way to label an e-mail as spam or non-spam is to set up a finite set of discriminative features and use a classifier for the detection. In most cases, the selection of such features is empirically verified. In this paper, two different methods are proposed to select the most discriminative features among a set of reasonably arbitrary features for spam e-mail detection. The selection methods are developed using the Common Vector Approach (CVA) which is actually a subspace-based pattern classifier. Experimental results indicate that the proposed feature selection methods give considerable reduction on the number of features without affecting recognition rates. © Springer-Verlag Berlin Heidelberg 2006.