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

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 to predict 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 the sets meta-features are extracted as well as the impact of each set of meta-features in 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 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.