Boosted random forest

The ability of generalization by random forests is higher than that by other multi-class classifiers because of the effect of bagging and feature selection. Since random forests based on ensemble learning requires a lot of decision trees to obtain high performance, it is not suitable for implementing the algorithm on the small-scale hardware such as embedded system. In this paper, we propose a boosted random forests in which boosting algorithm is introduced into random forests. Experimental results show that the proposed method, which consists of fewer decision trees, has higher generalization ability comparing to the conventional method.