  Dynamic Classifier Selection (DCS) techniques have difficulty in selecting
the most competent classifier in a pool, even when its presence is assured.
Since the DCS techniques rely only on local data to estimate a classifier's
competence, the manner in which the pool is generated could affect the choice
of the best classifier for a given sample. That is, the global perspective in
which pools are generated may not help the DCS techniques in selecting a
competent classifier for samples that are likely to be mislabelled. Thus, we
propose in this work an online pool generation method that produces a locally
accurate pool for test samples in difficult regions of the feature space. The
difficulty of a given area is determined by the classification difficulty of
the samples in it. That way, by using classifiers that were generated in a
local scope, it could be easier for the DCS techniques to select the best one
for the difficult samples. For the query samples in easy regions, a simple
nearest neighbors rule is used. In the extended version of this work, a deep
analysis on the correlation between instance hardness and the performance of
DCS techniques is presented. An instance hardness measure that conveys the
degree of local class overlap is then used to decide when the local pool is
used in the proposed scheme. The proposed method yielded significantly greater
recognition rates in comparison to a Bagging-generated pool and two other
global pool generation schemes for all DCS techniques evaluated. The proposed
scheme's performance was also significantly superior to three state-of-the-art
classification models and statistically equivalent to five of them. Moreover,
an extended analysis on the computational complexity of the proposed method and
of several DS techniques is presented in this version. We also provide the
implementation of the proposed technique using the DESLib library on GitHub.
