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Dynamic ensemble selection (DES) techniques work by estimating the level ofcompetence of each classifier from a pool of classifiers. Only the mostcompetent ones are selected to classify a given test sample. Hence, the keyissue in DES is the criterion used to estimate the level of competence of theclassifiers in predicting the label of a given test sample. In order to performa more robust ensemble selection, we proposed the META-DES framework usingmeta-learning, where multiple criteria are encoded as meta-features and arepassed down to a meta-classifier that is trained to estimate the competencelevel of a given classifier. In this technical report, we present astep-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 onthe estimation of the competence level of the base classifier. Moreover, ananalysis of the impact of several factors in the system performance, such asthe number of classifiers in the pool, the use of different linear baseclassifiers, as well as the size of the validation data. We show that using thedynamic selection of linear classifiers through the META-DES framework, we cansolve complex non-linear classification problems where other combinationtechniques such as AdaBoost cannot.

A DEEP analysis of the META-DES framework for dynamic selection of ensemble of classifiers