Bayesian weighted random forest for classification of high-dimensional genomics data

In this paper, a full Bayesian weighted probabilistic model is developed for random classification trees. The new model Bayesian Weighted Random Classification Forest (BWRCF) arises from the modification of the existing random classification forest in two ways. Firstly, the tree terminal node estimation procedure is replaced with a Bayesian estimation approach. Secondly, a new variable ranking procedure is developed and then hybridized with BWRCF to tackle the high-dimensionality issues. The performance of the proposed method is analyzed using simulated and real-life high-dimensional microarray datasets based on holdout accuracy and misclassification error rates. The results of the analyses showed that the proposed BWRCF is robust in terms of its ability to withstand moderate to large high-dimensionality scenarios. In addition, BWRCF also has improved predictive and efficiency abilities over selected competing methods.