这篇论文系统梳理了概念漂移检测算法，并用合成和真实数据测试了它们在突变和渐变场景下的表现，适合做这一方向基础研究的人参考。
该论文从理论上分析了概念漂移的特征，并分类讨论了多种漂移检测算法。在合成和真实数据集上评估了这些算法在不同漂移场景（如突变和渐变）下的性能。研究旨在加深对概念漂移行为及检测器适用性的理解。
Machine learning algorithms deployed for evolving streaming environments must handle the non-stationary data distributions, commonly referred to as concept drift. The presence of concept drift poses a major challenge for many real-world applications because it can severely degrade their predictive performance, hindering their ability to support robust decision-making. Consequently, the timely and efficient detection of drift events is critical for sustaining high accuracy over time. This study examines theoretically the concept drift characteristics and numerous drift detection algorithms across several categories. Furthermore, we evaluate their performance on both synthetic and real-world datasets exhibiting diverse streaming scenarios and drift characteristics, such as abrupt and gradual changes. This study aims to enhance understanding of the complex notion of concept drift characteristics and behavior of drift detectors, along with their applicability to diverse contexts.