  Service monitoring applications continuously produce data to monitor their
availability. Hence, it is critical to classify incoming data in real-time and
accurately. For this purpose, our study develops an adaptive classification
approach using Learn++ that can handle evolving data distributions. This
approach sequentially predicts and updates the monitoring model with new data,
gradually forgets past knowledge and identifies sudden concept drift. We employ
consecutive data chunks obtained from an industrial application to evaluate the
performance of the predictors incrementally.
