Research Article
Recurrent Adaptive Classifier Ensemble for Handling Recurring Concept Drifts
| Input: ( ) the streaming data chunks | | archive of ensemble models at time step t | | Diversity measure : Q Statistic | | Drift Detection Method Detect Drift | | Output:: the generalized ensemble model at each time step t | (1) | For each incoming data chunk do | (2) | Train new model with data chunk | (3) | Test with | (4) | drift Detect Drift ( ) | (5) | if drift == true | (6) | adapt models to current data | (7) | else | (8) | Update with to maximize diversity | (9) | End if | (10) | If | − 1| t then | (11) | {} | (12) | | (13) | | (14) | Endif | (15) | Calculate diversity of models | (16) | Output | (17) | Endif |
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