A design heuristic for hybrid classification ensembles in machine learning

Abstract

This paper presents a new design heuristic for hybrid classifier ensembles in machine learning. The heuristic entails inclusion of both global and local learners in the composition of base classifiers of a hybrid classifier ensemble, while also inducing both heterogeneous and homogenous diversity through the co-existence of global and local learners. Realization of the proposed heuristic is demonstrated within a hybrid ensemble classifier framework. The utility of proposed heuristic for enhancing the hybrid classifier ensemble performance is assessed and evaluated through a simulation study. Weka machine learning tool bench along with 46 datasets from the UCI machine learning repository are used. Simulation results indicate that the proposed heuristic enhances the performance of a hybrid classification ensemble.