  The imbalanced data classification remains a vital problem. The key is to
find such methods that classify both the minority and majority class correctly.
The paper presents the classifier ensemble for classifying binary,
non-stationary and imbalanced data streams where the Hellinger Distance is used
to prune the ensemble. The paper includes an experimental evaluation of the
method based on the conducted experiments. The first one checks the impact of
the base classifier type on the quality of the classification. In the second
experiment, the Hellinger Distance Weighted Ensemble (HDWE) method is compared
to selected state-of-the-art methods using a statistical test with two base
classifiers. The method was profoundly tested based on many imbalanced data
streams and obtained results proved the HDWE method's usefulness.
