  Class imbalance is a characteristic known for making learning more
challenging for classification models as they may end up biased towards the
majority class. A promising approach among the ensemble-based methods in the
context of imbalance learning is Dynamic Selection (DS). DS techniques single
out a subset of the classifiers in the ensemble to label each given unknown
sample according to their estimated competence in the area surrounding the
query. Because only a small region is taken into account in the selection
scheme, the global class disproportion may have less impact over the system's
performance. However, the presence of local class overlap may severely hinder
the DS techniques' performance over imbalanced distributions as it not only
exacerbates the effects of the under-representation but also introduces
ambiguous and possibly unreliable samples to the competence estimation process.
Thus, in this work, we propose a DS technique which attempts to minimize the
effects of the local class overlap during the classifier selection procedure.
The proposed method iteratively removes from the target region the instance
perceived as the hardest to classify until a classifier is deemed competent to
label the query sample. The known samples are characterized using instance
hardness measures that quantify the local class overlap. Experimental results
show that the proposed technique can significantly outperform the baseline as
well as several other DS techniques, suggesting its suitability for dealing
with class under-representation and overlap. Furthermore, the proposed
technique still yielded competitive results when using an under-sampled, less
overlapped version of the labelled sets, specially over the problems with a
high proportion of minority class samples in overlap areas. Code available at
https://github.com/marianaasouza/lords.
