  This work is aimed at the experimental studying the influence of local data
characteristics and drifts on the difficulties of learning various online
classifiers from multi-class imbalanced data streams. Firstly we present a
categorization of these data factors and drifts in the context of imbalanced
streams, then we introduce the generators of synthetic streams that model these
factors and drifts. The results of many experiments with synthetically
generated data streams have shown a much greater role of the overlapping
between many minority classes (the type of borderline examples) than for
streams with one minority class. The presence of rare examples in the stream is
the most difficult single factor. The local drift of splitting minority classes
is the third influential factor. Unlike binary streams, the specialized UOB and
OOB classifiers perform well enough for even high imbalance ratios. The most
challenging for all classifiers are complex scenarios integrating the drifts of
the identified factors simultaneously, which worsen the evaluation measures in
the case of a several minority classes stronger than for binary ones. This is
an extended version of the short paper presented at LIDTA'2022 workshop at
ECMLPKDD2022.
