  Dynamic Ensemble Selection (DES) techniques aim to select locally 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 (its the nearest neighbors in the validation
set). The K-Nearest Oracles Eliminate (KNORA-E) DES selects all classifiers
that correctly classify all samples in the region of competence of the test
sample, if such classifier exists, otherwise, it removes from the region of
competence the sample that is furthest from the test sample, and the process
repeats. When the region of competence has samples of different classes,
KNORA-E can reduce the region of competence in such a way that only samples of
a single class remain in the region of competence, leading to the selection of
locally incompetent classifiers that classify all samples in the region of
competence as being from the same class. In this paper, we propose two DES
techniques: K-Nearest Oracles Borderline (KNORA-B) and K-Nearest Oracles
Borderline Imbalanced (KNORA-BI). KNORA-B is a DES technique based on KNORA-E
that reduces the region of competence but maintains at least one sample from
each class that is in the original region of competence. KNORA-BI is a
variation of KNORA-B for imbalance datasets that reduces the region of
competence but maintains at least one minority class sample if there is any in
the original region of competence. Experiments are conducted comparing the
proposed techniques with 19 DES techniques from the literature using 40
datasets. The results show that the proposed techniques achieved interesting
results, with KNORA-BI outperforming state-of-art techniques.
