Research Article
Recurrent Adaptive Classifier Ensemble for Handling Recurring Concept Drifts
| Input: () chunks of streaming data | | M: a set of diverse models previously learned | | Output :: the generalized ensemble model at time step t | (1) | For each data chunk do | (2) | Learn a new base model with | (3) | Select transferred models by transferring the highly diverse stored models M | (4) | Build the generalized ensemble using the transferred models and the newly learned model | (5) | Update M with to maximise diversity | (6) | Endfor |
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