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