Characterization measures of ensemble systems using a meta-learning approach

In a decision making process, we are usually oriented to take into consideration all the relevant features (characteristics) involved in a specific problem. In Machine Learning, for instance, a decision is made through the use of a learning algorithm and the characterization process is represented by the corresponding datasets. In this context, classification algorithms can be applied, individually or through the use of ensemble systems (combination of classification methods), in the decision-making process. The concept of ensemble systems has emerged in the last decades as a strategy for combining independent classifiers (components), aiming to provide a decision that is potentially more effective than any single component. However, the design of the ensemble structure is not an easy task and it can have an important impact in the performance of these systems. In this paper, we investigate the use of meta-learning on the selection of the best configuration parameters (learning strategy, size and individual classifiers) for homogeneous structures of ensemble systems. The main aim of this analysis is to assess the effect of using meta-learning in the design of efficient and robust ensemble systems.