Beyond applying machine learning predictive models to static tasks, a significant corpus of research exists that applies machine learning predictive models to streaming environments that incur concept drift. With the prevalence of streaming real-world applications that are associated with changes in the underlying data distribution, the need for applications that are capable of adapting to evolving and time-varying dynamic environments can be hardly overstated. Dynamic environments are nonstationary and change with time and the target variables to be predicted by the learning algorithm and often evolve with time, a phenomenon known as concept drift. Most work in handling concept drift focuses on updating the prediction model so that it can recover from concept drift while little effort has been dedicated to the formulation of a learning system that is capable of learning different types of drifting concepts at any time with minimum overheads. This work proposes a novel and evolving data stream classifier called Adaptive Diversified Ensemble Selection Classifier (ADES) that significantly optimizes adaptation to different types of concept drifts at any time and improves convergence to new concepts by exploiting different amounts of ensemble diversity. The ADES algorithm generates diverse base classifiers, thereby optimizing the margin distribution to exploit ensemble diversity to formulate an ensemble classifier that generalizes well to unseen instances and provides fast recovery from different types of concept drift. Empirical experiments conducted on both artificial and real-world data streams demonstrate that ADES can adapt to different types of drifts at any given time. The prediction performance of ADES is compared to three other ensemble classifiers designed to handle concept drift using both artificial and real-world data streams. The comparative evaluation performed demonstrated the ability of ADES to handle different types of concept drifts. The experimental results, including statistical test results, indicate comparable performances with other algorithms designed to handle concept drift and prove their significance and effectiveness.

中文翻译：

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ADES：一种新的基于集合多样性的处理概念漂移的方法

除了将机器学习预测模型应用于静态任务之外，还有大量研究将机器学习预测模型应用于导致概念漂移的流媒体环境。随着与底层数据分布变化相关的流式真实世界应用程序的流行，对能够适应不断发展和随时间变化的动态环境的应用程序的需求再怎么强调也不为过。动态环境是非平稳的，随时间变化，学习算法要预测的目标变量经常随时间变化，这种现象称为概念漂移。大多数处理概念漂移的工作都集中在更新预测模型上，以便它可以从概念漂移中恢复，而很少致力于制定一个学习系统，该系统能够以最小的开销随时学习不同类型的漂移概念。这项工作提出了一种新颖且不断发展的数据流分类器，称为自适应多样化集合选择分类器 (ADES)，该分类器可随时显着优化对不同类型概念漂移的适应，并通过利用不同数量的集合多样性来提高对新概念的收敛性。ADES 算法生成不同的基分类器，从而优化边缘分布以利用集成多样性来制定集成分类器，该分类器可以很好地泛化到未见过的实例并提供从不同类型的概念漂移中快速恢复。在人工和现实世界数据流上进行的实证实验表明，ADES 可以在任何给定时间适应不同类型的漂移。将 ADES 的预测性能与其他三个集成分类器进行比较，这些分类器旨在使用人工和现实世界的数据流处理概念漂移。进行的比较评估证明了 ADES 处理不同类型概念漂移的能力。实验结果，包括统计测试结果，表明与其他旨在处理概念漂移的算法的性能相当，并证明了它们的重要性和有效性。