A Broad Ensemble Learning System for Drifting Stream Classification

Data stream classification has become a major research topic due to the increase in temporal data. One of the biggest hurdles of data stream classification is the development of algorithms that deal with evolving data, also known as concept drifts. As data changes over time, static prediction models lose their validity. Adapting to concept drifts provides more robust and better performing models. The Broad Learning System (BLS) is an effective broad neural architecture recently developed for incremental learning. BLS cannot provide instant response since it requires huge data chunks and is unable to handle concept drifts. We propose a Broad Ensemble Learning System (BELS) for stream classification with concept drift. BELS uses a novel updating method that greatly improves best-in-class model accuracy. It employs a dynamic output ensemble layer to address the limitations of BLS. We present its mathematical derivation, provide comprehensive experiments with 11 datasets that demonstrate the adaptability of our model, including a comparison of our model with BLS, and provide parameter and robustness analysis on several drifting streams, showing that it statistically significantly outperforms seven state-of-the-art baselines. We show that our proposed method improves on average 44% compared to BLS, and 29% compared to other competitive baselines.