  The key issue in Dynamic Ensemble Selection (DES) is defining a suitable
criterion for calculating the classifiers' competence. There are several
criteria available to measure the level of competence of base classifiers, such
as local accuracy estimates and ranking. However, using only one criterion may
lead to a poor estimation of the classifier's competence. In order to deal with
this issue, we have proposed a novel dynamic ensemble selection framework using
meta-learning, called META-DES. An important aspect of the META-DES framework
is that multiple criteria can be embedded in the system encoded as different
sets of meta-features. However, some DES criteria are not suitable for every
classification problem. For instance, local accuracy estimates may produce poor
results when there is a high degree of overlap between the classes. Moreover, a
higher classification accuracy can be obtained if the performance of the
meta-classifier is optimized for the corresponding data. In this paper, we
propose a novel version of the META-DES framework based on the formal
definition of the Oracle, called META-DES.Oracle. The Oracle is an abstract
method that represents an ideal classifier selection scheme. A meta-feature
selection scheme using an overfitting cautious Binary Particle Swarm
Optimization (BPSO) is proposed for improving the performance of the
meta-classifier. The difference between the outputs obtained by the
meta-classifier and those presented by the Oracle is minimized. Thus, the
meta-classifier is expected to obtain results that are similar to the Oracle.
Experiments carried out using 30 classification problems demonstrate that the
optimization procedure based on the Oracle definition leads to a significant
improvement in classification accuracy when compared to previous versions of
the META-DES framework and other state-of-the-art DES techniques.
