  Dynamic classifier selection systems aim to select a group of classifiers
that is most adequate for a specific query pattern. This is done by defining a
region around the query pattern and analyzing the competence of the classifiers
in this region. However, the regions are often surrounded by noise which can
difficult the classifier selection. This fact makes the performance of most
dynamic selection systems no better than static selections. In this paper, we
demonstrate that the performance dynamic selection systems end up limited by
the quality of the regions extracted. Thereafter, we propose a new dynamic
classifier selection that improves the regions of competence in order to
achieve higher recognition rates. obtained from several classification
databases show the proposed method not only increase the recognition
performance but also decreases the computational cost.
