Improve the Performance of Random Forests by Introducing Weight Update Technique

We investigate approaches to improve the performance of random forests by introducing weight update and bootstrap techniques and propose a new algorithm that combine these techniques smoothly. Experiments show that the proposed approach performs better than the original RF and works well with different weight update techniques used by three most popular version of AdaBoost. At the same time there is no more parameters to adjust compared with RF.