Voting multiple classifiers decisions for spam detection

Abstract

A considerable amount of research and technology development has been emerged to address the problem of spam detection. Based on a Boolean cellular approach and naïve Bayes technique built as individual classifiers, we evaluate a novel method that combines these two classifiers to determine whether we can more accurately detect Spam. Experimental results show that the proposed combination increases the classification performance as measured on LingSpam dataset.