Boosting Naïve Bayes text classification using uncertainty-based selective sampling

This paper presents adaptive boosting with uncertainty-based selective sampling (AdaBUS), a variant of the AdaBoost algorithm for boosting the Naïve Bayes (NB) text classification. Although the boosting technique has been shown to effectively improve the accuracy of machine-learning-based classifiers, boosting does not work well with NB text classification owing to the low variance in the accuracy of its base classifier. In this study, we propose boosting the NB text classifier by combining the AdaBoost boosting algorithm with uncertainty-based selective sampling. Experiments using the popular Reuters-21578 document collection showed that the proposed algorithm effectively improves classification accuracy.