A design heuristic for hybrid classification ensembles in machine learning

Article type: Research Article

Authors: Baumgartner, Dustin | Serpen, Gursel^{; *}

Affiliations: Electrical Engineering and Computer Science Department, University of Toledo, Toledo, OH, USA

Correspondence: [*] Corresponding author: Gursel Serpen, Electrical Engineering and Computer Science Department, University of Toledo, Toledo, OH 43606, USA. E-mail: gursel.serpen@utoledo.edu

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.

Keywords: Machine learning, ensemble classifier, hybrid ensemble, ensemble design heuristic, global-local learner, heterogeneous-homogeneous diversity

DOI: 10.3233/IDA-2012-0521

Journal: Intelligent Data Analysis, vol. 16, no. 2, pp. 233-246, 2012