Weighted Random Forest Algorithm Based on Bayesian Algorithm

Abstract

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