An enormous and ever-growing volume of data is nowadays becoming available in a sequential fashion in various real-world applications. Learning in nonstationary environments constitutes a major challenge, and this problem becomes orders of magnitude more complex in the presence of class imbalance. We provide new insights into learning from nonstationary and imbalanced data in online learning, a largely unexplored area. We propose the novel Adaptive REBAlancing (AREBA) algorithm that selectively includes in the training set a subset of the majority and minority examples that appeared so far, while at its heart lies an adaptive mechanism to continually maintain the class balance between the selected examples. We compare AREBA with strong baselines and other state-of-the-art algorithms and perform extensive experimental work in scenarios with various class imbalance rates and different concept drift types on both synthetic and real-world data. AREBA significantly outperforms the rest with respect to both learning speed and learning quality. Our code is made publicly available to the scientific community.

中文翻译：

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非平稳环境中自适应重新平衡的在线学习

如今，在各种实际应用程序中，大量且不断增长的数据以顺序方式可用。在非平稳环境中学习构成了一项重大挑战，在存在类别不平衡的情况下，这个问题变得更加复杂。我们提供了从在线学习中的非平稳和不平衡数据中学习的新见解，这是一个很大程度上未开发的领域。我们提出了新颖的自适应 REBAlancing (AREBA) 算法，该算法有选择地将目前出现的多数和少数示例的子集包含在训练集中，而其核心在于一种自适应机制，以持续保持所选示例之间的类平衡。我们将 AREBA 与强基线和其他最先进的算法进行比较，并在合成和现实世界数据的各种类别不平衡率和不同概念漂移类型的场景中执行广泛的实验工作。AREBA 在学习速度和学习质量方面都明显优于其他的。我们的代码向科学界公开。