Bayesian regularized neural network decision tree ensemble model for genomic data classification

Abstract

Machine learning techniques have been widely applied to solve the classification problem of highly dimensional and complex data in the field of bioinformatics. Among them, Bayesian regularized neural network (BRNN) became one of the popular choices due to its robustness and ability to avoid over fitting. On the other hand, Bayesian approach applied to neural network training offers computational burden and increases its time complexity. This restricts the use of BRNN in an on-line machine learning system. In this article, a Bayesian regularized neural network decision Tree (BrNdT) ensemble model, is proposed to combat high computational time complexity of a classifier model. The key idea behind the proposed ensemble methodology is to weigh and combine several individual classifiers and apply majority voting decision scheme to obtain an efficient classifier which outperforms each one of them. The simulation results show that the proposed method achieves a significant reduction in time complexity and maintains high accuracy over other conventional techniques.