Weighted vote for trees aggregation in Random Forest

Random Forest RF is a successful technique of ensemble prediction that uses the majority voting or an average depending on the combination. However, it is clear that each tree in a random forest can have different contribution to the treatment of some instance. In this paper, we show that the prediction performance of RF´s can still be improved by replacing the GINI index with another index (twoing or deviance). Our experiments also indicate that weighted voting gives better results compared to the majority vote.