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In a data stream environment, classification models must handle concept driftefficiently and effectively. Ensemble methods are widely used for this purpose;however, the ones available in the literature either use a large data chunk toupdate the model or learn the data one by one. In the former, the model maymiss the changes in the data distribution, and in the latter, the model maysuffer from inefficiency and instability. To address these issues, we introducea novel ensemble approach based on the Broad Learning System (BLS), where minichunks are used at each update. BLS is an effective lightweight neuralarchitecture recently developed for incremental learning. Although it is fast,it requires huge data chunks for effective updates, and is unable to handledynamic changes observed in data streams. Our proposed approach named BroadEnsemble Learning System (BELS) uses a novel updating method that significantlyimproves best-in-class model accuracy. It employs an ensemble of output layersto address the limitations of BLS and handle drifts. Our model tracks thechanges in the accuracy of the ensemble components and react to these changes.We present the mathematical derivation of BELS, perform comprehensiveexperiments with 20 datasets that demonstrate the adaptability of our model tovarious drift types, and provide hyperparameter and ablation analysis of ourproposed model. Our experiments show that the proposed approach outperformsnine state-of-the-art baselines and supplies an overall improvement of 13.28%in terms of average prequential accuracy.

A Broad Ensemble Learning System for Drifting Stream Classification