  Modern streaming data categorization faces significant challenges from
concept drift and class imbalanced data. This negatively impacts the output of
the classifier, leading to improper classification. Furthermore, other factors
such as the overlapping of multiple classes limit the extent of the correctness
of the output. This work proposes a novel framework for integrating data
pre-processing and dynamic ensemble selection, by formulating the
classification framework for the nonstationary drifting imbalanced data stream,
which employs the data pre-processing and dynamic ensemble selection
techniques. The proposed framework was evaluated using six artificially
generated data streams with differing imbalance ratios in combination with two
different types of concept drifts. Each stream is composed of 200 chunks of 500
objects described by eight features and contains five concept drifts. Seven
pre-processing techniques and two dynamic ensemble selection methods were
considered. According to experimental results, data pre-processing combined
with Dynamic Ensemble Selection techniques significantly delivers more accuracy
when dealing with imbalanced data streams.
