  In real-world applications, the process generating the data might suffer from
nonstationary effects (e.g., due to seasonality, faults affecting sensors or
actuators, and changes in the users' behaviour). These changes, often called
concept drift, might induce severe (potentially catastrophic) impacts on
trained learning models that become obsolete over time, and inadequate to solve
the task at hand. Learning in presence of concept drift aims at designing
machine and deep learning models that are able to track and adapt to concept
drift. Typically, techniques to handle concept drift are either active or
passive, and traditionally, these have been considered to be mutually
exclusive. Active techniques use an explicit drift detection mechanism, and
re-train the learning algorithm when concept drift is detected. Passive
techniques use an implicit method to deal with drift, and continually update
the model using incremental learning. Differently from what present in the
literature, we propose a hybrid alternative which merges the two approaches,
hence, leveraging on their advantages. The proposed method called
Hybrid-Adaptive REBAlancing (HAREBA) significantly outperforms strong baselines
and state-of-the-art methods in terms of learning quality and speed; we
experiment how it is effective under severe class imbalance levels too.
