  Dynamic ensemble selection (DES) techniques work by estimating the level of
competence of each classifier from a pool of classifiers. Only the most
competent ones are selected to classify a given test sample. Hence, the key
issue in DES is the criterion used to estimate the level of competence of the
classifiers in predicting the label of a given test sample. In order to perform
a more robust ensemble selection, we proposed the META-DES framework using
meta-learning, where multiple criteria are encoded as meta-features and are
passed down to a meta-classifier that is trained to estimate the competence
level of a given classifier. In this technical report, we present a
step-by-step analysis of each phase of the framework during training and test.
We show how each set of meta-features is extracted as well as their impact on
the estimation of the competence level of the base classifier. Moreover, an
analysis of the impact of several factors in the system performance, such as
the number of classifiers in the pool, the use of different linear base
classifiers, as well as the size of the validation data. We show that using the
dynamic selection of linear classifiers through the META-DES framework, we can
solve complex non-linear classification problems where other combination
techniques such as AdaBoost cannot.
