FIRE-DES++: Enhanced Online Pruning of Base Classifiers for Dynamic Ensemble Selection

Dynamic Ensemble Selection (DES) techniques aim to select one or more competent classifiers for the classification of each new test sample. Most DES techniques estimate the competence of classifiers using a given criterion over the region of competence of the test sample, usually defined as the set of nearest neighbors of the test sample in the validation set.