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. Despite being very effective in several classification tasks, DES techniques can select classifiers that classify all samples in the region of competence as being from the same class. The Frienemy Indecision REgion DES (FIRE-DES) tackles this problem by pre-selecting classifiers that correctly classify at least one pair of samples from different classes in the region of competence of the test sample. However, FIRE-DES applies the pre-selection for the classification of a test sample if and only if its region of competence is composed of samples from different classes (indecision region), even though this criterion is not reliable for determining if a test sample is located close to the borders of classes (true indecision region) when the region of competence is obtained using classical nearest neighbors approach. To tackle these issues, we propose the FIRE-DES, an enhanced FIRE-DES that removes noise and reduces the overlap of classes in the validation set; and defines the region of competence using an equal number of samples of each class, avoiding selecting a region of competence with samples of a single class. Experimental results show that FIRE-DES increases the classification performance of all DES techniques considered in this work, outperforming FIRE-DES with 7 out of the 8 DES techniques, and outperforming state-of-the-art DES frameworks. Keywords: Ensemble of classifiers, Dynamic ensemble selection, Classifier competence, Prototype selection 1. Introduction Dynamic Ensemble Selection (DES) has become an important research topic in the last few years [1]. Given a test sample and a pool of classifiers, DES techniques select one or more competent classifiers for the classification of that test sample. The most important part in DES techniques is how to evaluate the competence level of each base classifier for the classification of a given test sample [2].