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The growth of email users has resulted in the dramatic increasing of the spam emails. Helpfully, there are different approaches able to automatically detect and remove most of these messages, and the best-known ones are based on Bayesian decision theory and Support Vector Machines. However, there are several forms of Naive Bayes filters, something the anti-spam literature does not always acknowledge. In this paper, we discuss seven different versions of Naive Bayes classifiers, and compare them with the well-known Linear Support Vector Machine on six non-encoded datasets. Moreover, we propose a new measurement in order to evaluate the quality of anti-spam classifiers. In this way, we investigate the benefits of using Matthews correlation coefficient as the measure of performance.