At present, dynamic data stream classification has achieved many successful results through concept drift detection and ensemble learning. However, generally, due to delay in concept drift detection, the active classifier may further learn data belonging to a new concept. This will ultimately degrade the generalization capability of this active classifier on its corresponding concept. Thus, how can a classifier corresponding to one concept unlearns the learned data belonging to another concept? Two unlearning algorithms are proposed to address this problem. The first one based on the passive-a...
