  Learning from an imbalanced dataset is a tricky proposition. Because these
datasets are biased towards one class, most existing classifiers tend not to
perform well on minority class examples. Conventional classifiers usually aim
to optimize the overall accuracy without considering the relative distribution
of each class. This article presents a superensemble classifier, to tackle and
improve predictions in imbalanced classification problems, that maps Hellinger
distance decision trees (HDDT) into radial basis function network (RBFN)
framework. Regularity conditions for universal consistency and the idea of
parameter optimization of the proposed model are provided. The proposed
distribution-free model can be applied for feature selection cum imbalanced
classification problems. We have also provided enough numerical evidence using
various real-life data sets to assess the performance of the proposed model.
Its effectiveness and competitiveness with respect to different
state-of-the-art models are shown.
