Improving BAS Committee with ETL Voting

Boosting is a machine learning technique that combines several weak classifiers to improve the overall accuracy. A well known algorithm based on boosting is AdaBoost. Boosting at start (BAS) is a boosting framework that generalizes AdaBoost by allowing any initial weight distribution. BAS Committee is a scheme that uses feature clustering to determine the best weight assignments in the BAS framework. One of the drawbacks of BAS committee is its final step which uses a simple majority voting approach over the chosen classifiers. Entropy guided transformation learning (ETL) is a machine learning strategy that combines decision trees and transformation based learning avoiding the explicit need of template design. Here, we present ETL voting BAS committee, a scheme that combines ETL and BAS Committee in order to determine the best combination for the classifiers of the ensemble. Besides that, since no extra assumption is made, ETL voting is generic and can be used in any committee approach. Our empirical findings indicate that the BAS performance can be improved with a new combination of the classifiers determined by ETL voting.