Investigating the influence of the choice of the ensemble members in accuracy and diversity of selection-based and fusion-based methods for ensembles

One of the most important steps in the design of a multi-classifier system (MCS), also known as ensemble, is the choice of the components (classifiers). This step is very important to the overall performance of a MCS since the combination of a set of identical classifiers will not outperform the individual members. The ideal situation would be a set of classifiers with uncorrelated errors – they would be combined in such a way as to minimize the effect of these failures. This paper presents an extensive evaluation of how the choice of the components (classifiers) can affect the performance of several combination methods (selection-based and fusion-based methods). An analysis of the diversity of the MCSs when varying their components is also performed. As a result of this analysis, it is aimed to help designers in the choice of the individual classifiers and combination methods of an ensemble.