  In dynamic selection (DS) techniques, only the most competent classifiers,
for the classification of a specific test sample are selected to predict the
sample's class labels. The more important step in DES techniques is estimating
the competence of the base classifiers for the classification of each specific
test sample. The classifiers' competence is usually estimated using the
neighborhood of the test sample defined on the validation samples, called the
region of competence. Thus, the performance of DS techniques is sensitive to
the distribution of the validation set. In this paper, we evaluate six
prototype selection techniques that work by editing the validation data in
order to remove noise and redundant instances. Experiments conducted using
several state-of-the-art DS techniques over 30 classification problems
demonstrate that by using prototype selection techniques we can improve the
classification accuracy of DS techniques and also significantly reduce the
computational cost involved.
