Instance based random forest with rotated feature space

Random Forest is a competitive ensemble method in the field of machine learning with several advantages such as efficiency, robustness, generalization, ease of implementation, etc. This study attempts to increase the diversity among the pairwise individuals in the forest. On the other hand, we propose an instance based method to select several superior trees to perform the voting. The proposed method is evaluated on 28 datasets from the UCI Repository.