  Ensemble learning methods are designed to benefit from multiple learning
algorithms for better predictive performance. The tradeoff of this improved
performance is slower speed and larger size of ensemble learning systems
compared to single learning systems. In this paper, we present a novel approach
to deal with this problem in Random Forest (RF) as one of the most powerful
ensemble methods. The method is based on crossbreeding of the best tree
branches to increase the performance of RF in space and speed while keeping the
performance in the classification measures. The proposed approach has been
tested on a group of synthetic and real datasets and compared to the standard
RF approach. Several evaluations have been conducted to determine the effects
of the Crossbred RF (CRF) on the accuracy and the number of trees in a forest.
The results show better performance of CRF compared to RF.
