An Empirical Analysis of Meta-learning for the Automatic Choice of Architecture and Components in Ensemble Systems

Studies with ensemble systems have gained attention recently and, most of them, propose new methods for the design (generation) of different components in these systems. In parallel, new contributions of meta-learning have been presented as an efficient alternative to automatic recommendation of algorithms. In this paper, we apply meta-learning in the process of recommendation of important parameters of ensemble systems, which are: architecture and individual classifiers. The main goal is to provide an efficient way to design ensemble systems. In order to validate the proposed approach, an empirical investigation is conducted, recommending three possible types of ensemble architectures (Bagging, Boosting and Multi-Boosting) and five possible types of learning algorithms to compose the ensemble systems (individual classifiers or components). An initial analysis of the results confirms that meta-learning can be a promising tool to be used in the automatic choice of important parameters in ensemble systems.