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.

翻译：在数据流环境中，分类模型必须高效且有效地处理概念漂移。集成方法因其在此类场景中的优势而被广泛采用；然而，现有文献中的集成方法要么使用大数据块更新模型，要么逐个学习数据。前者可能导致模型错过数据分布的变化，而后者则可能遭遇效率低下和稳定性不足的问题。为应对这些挑战，我们提出了一种基于宽学习系统（BLS）的新型集成方法，其中每次更新使用小数据块。BLS是一种近期开发的轻量级神经网络架构，专为增量学习设计。尽管其速度快，但需要大量数据块才能有效更新，且无法处理数据流中动态变化的问题。我们提出的方法称为宽集成学习系统（BELS），采用一种新颖的更新方法，显著提升了同类最佳模型的准确率。它利用输出层集成来克服BLS的局限性并处理漂移。我们的模型跟踪集成组件准确率的变化，并针对这些变化做出响应。我们给出了BELS的数学推导，使用20个数据集进行了全面实验，证明了模型对不同漂移类型的适应性，并对所提模型进行了超参数与消融分析。实验结果表明，所提方法优于九种最先进基线模型，在前置序列准确率方面平均提升了13.28%。