An Empirical Evaluation of Boosting-BAN and Boosting-MultiTAN

Abstract

An ensemble consists of a set of independently trained classifiers whose predictions are combined when classifying novel instances. Previous research has shown that an ensemble as a whole is often more accurate than any of the single classifiers in the ensemble. Boosting-BAN classifier is considered stronger than Boosting-MultiTAN on noise-free data. However, there are strong empirical indications that Boosting-MultiTAN is much more robust than Boosting-BAN in noisy settings. For this reason, in this paper we built an ensemble using a voting methodology of Boosting-BAN and Boosting-MultiTAN ensembles with 10 sub-classifiers in each one. We performed a comparison with Boosting-BAN and Boosting-MultiTAN ensembles with 25 sub-classifiers on standard benchmark datasets and the proposed technique was the most accurate.

An Ensemble of Fine-Tuned Heterogeneous Bayesian Classifiers

“…Their experimental results show that the Boosting-BAN has higher classification accuracy than Boosting-MultiTAN on noise-free data. Moreover, Sun and Zhou [17] built an ensemble combing Boosting-BAN and Boosting-MultiTAN using the sum voting methodology. The sum rule adds all confidence scores of sub-ensemble Prediction for each class and the class with the highest sum wins the election.…”