Boosted Voting Scheme on Classification

Ensemble of classifiers has been an interesting research topic in the area of machine learning. In this paper, we propose a new ensemble scheme which focuses on driving the relationship between multiple learning algorithms and variant data distributions. The advantage of the framework can form an expressive hypotheses combination allowing a set of learning algorithms with respect to the data distributions, instead of majority voting scheme which was commonly employed for improving the prediction stability or a weak learning algorithm needed in bagging/boosting/random-forest algorithm. Experimental results on several UCI benchmark datasets demonstrate that the proposed scheme gains a worthwhile valuable performance on the classification learning task.