  Dynamic ensemble selection systems 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. This is achieved by defining a
criterion to measure the level of competence of a base classifier, such as, its
accuracy in local regions of the feature space around the query instance.
However, using only one criterion about the behavior of a base classifier is
not sufficient to accurately estimate its level of competence. In this paper,
we present a novel dynamic ensemble selection framework using meta-learning. We
propose five distinct sets of meta-features, each one corresponding to a
different criterion to measure the level of competence of a classifier for the
classification of input samples. The meta-features are extracted from the
training data and used to train a meta-classifier to predict whether or not a
base classifier is competent enough to classify an input instance. During the
generalization phase, the meta-features are extracted from the query instance
and passed down as input to the meta-classifier. The meta-classifier estimates,
whether a base classifier is competent enough to be added to the ensemble.
Experiments are conducted over several small sample size classification
problems, i.e., problems with a high degree of uncertainty due to the lack of
training data. Experimental results show the proposed meta-learning framework
greatly improves classification accuracy when compared against current
state-of-the-art dynamic ensemble selection techniques.
