Abstract
This paper presents adaptive boosting with uncertainty-based selective sampling (AdaBUS), a variant of the AdaBoost algorithm for boosting the Naı¨ve 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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.