分析多种漂移检测方法，评估其在真实与合成数据上的表现。
Learner-based Concept Drift Detection: Analysis and Evaluation
- 从理论和实验双角度分析概念漂移特性及检测算法
- 在多种流数据场景下测试算法对突变与渐变漂移的响应能力
- 为实际应用提供漂移检测方法选型参考
部署于动态流环境中的机器学习算法需应对非平稳数据分布问题，即概念漂移。概念漂移会严重降低模型预测性能，影响决策可靠性。因此，及时高效地检测漂移事件对维持长期高精度至关重要。本研究从理论上分析了概念漂移特征及多类漂移检测算法，并在包含突变与渐变等不同特性的合成与真实世界数据集上进行评估。旨在深化对概念漂移复杂特性和检测器行为的理解，明确其在多样化场景中的适用性。
原文摘要 · Abstract (English)
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.
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