Abstract
Random Forest is an ensemble learning method used for classification and regression. In such an ensemble, multiple classifiers are used where each classifier casts one vote for its predicted class label. Majority voting is then used to determine the class label for unlabelled instances. Since it has been proven empirically that ensembles tend to yield better results when there is a significant diversity among the constituent models, many extensions were developed during the past decade that aim at inducing some diversity in the constituent models in order to improve the performance of Random Forests in terms of both speed and accuracy. In this paper, we propose a method to promote Random Forest diversity by using randomly selected subspaces, giving a weight to each subspace according to its predictive power, and using this weight in majority voting. Experimental study on 15 real datasets showed favourable results, demonstrating the potential of the proposed method.