Selective ensemble learning with parallel optimization and hierarchical selection

Abstract

Selective ensemble learning is a method that selects a subset of diverse and accurate base models to generate stronger generalization ability. In this paper, we propose a selective ensemble learning algorithm called PTHS and a novel feature selection method called MSRD to solve the problem of high dimensionality. The algorithm PTHS uses a parallel optimization and hierarchical selection framework. The experimental result showed that MSRD is a suitable feature selection method for solving the problem of high dimensionality and that PTHS achieved better performance than other methods.