Fusion Ensemble Modeling Method Using Bayesian Inference and Binary Tree Algorithm

The traditional ensemble modeling method has problems such as complex calculation, low precision, and insufficient generalization performance. Therefore, this article proposes three modeling methods based on Bayesian inference and binary tree fusion ensembled (BTFE). Among them, Bayesian inference are used as the selection rule and to distinguish the adaptability of sub-model. Three different modeling methods based on Bagging and binary tree are proposed to replace the traditional complex ensemble model, which reduces the training difficulty of the model and improves the interpretability of the ensemble model. The binary tree and Bayesian inference fusion method is proposed to construct the selective ensemble model. Experimental results on benchmark data sets show the effectiveness of the proposed method.