  A fundamental issue for statistical classification models in a streaming
environment is that the joint distribution between predictor and response
variables changes over time (a phenomenon also known as concept drifts), such
that their classification performance deteriorates dramatically. In this paper,
we first present a hierarchical hypothesis testing (HHT) framework that can
detect and also adapt to various concept drift types (e.g., recurrent or
irregular, gradual or abrupt), even in the presence of imbalanced data labels.
A novel concept drift detector, namely Hierarchical Linear Four Rates (HLFR),
is implemented under the HHT framework thereafter. By substituting a
widely-acknowledged retraining scheme with an adaptive training strategy, we
further demonstrate that the concept drift adaptation capability of HLFR can be
significantly boosted. The theoretical analysis on the Type-I and Type-II
errors of HLFR is also performed. Experiments on both simulated and real-world
datasets illustrate that our methods outperform state-of-the-art methods in
terms of detection precision, detection delay as well as the adaptability
across different concept drift types.
