Weighted Random Forest Algorithm Based on Bayesian Algorithm

Abstract The random forest(RF) algorithm is a very efficient and excellent ensemble classification algorithm. In this paper, we improve the forest propose an called ‘Bayesian Weighted Random Forest’(B-RF), focus on problem that inaccurate decision tree caused by same voting weights in traditional model. main idea underlying proposed model to replace supermajority of forests into weighted voting, fully consider difference ability each tree, using Bayesian formula dynamically update weight value for so strong classifier has higher power effectively improves overall performance classification. Through verification UCI database, results show accuracy higher. This illustrate outperformance comparison with general