论文标题
概念漂移检测的集体分配监视
Class Distribution Monitoring for Concept Drift Detection
论文作者
论文摘要
我们介绍了类别分配监视（CDM），这是一种有效的概念 - 拖船检测方案，可监视数据式流的类别条件分布。特别是，我们的解决方案利用了基于Quanttree的在线和非参数变更检测算法的多个实例。 CDM报告了检测到任何类别的分布变化后的概念漂移，从而确定哪个类受到概念漂移的影响。这可能是用于诊断和适应的宝贵信息。我们对合成和现实世界数据集的实验表明，当概念漂移影响几个类时，CDM优于监视整体数据分布的算法，而当漂移影响所有类时，同时实现了类似的检测延迟。此外，CDM优于监视分类误差的可比较方法，尤其是当更改不是很明显的情况下。最后，我们证明CDM继承了基础变更检测器的属性，从而有效控制了错误警报之前的预期时间，或者平均运行长度（ARL $ _0 $）。
We introduce Class Distribution Monitoring (CDM), an effective concept-drift detection scheme that monitors the class-conditional distributions of a datastream. In particular, our solution leverages multiple instances of an online and nonparametric change-detection algorithm based on QuantTree. CDM reports a concept drift after detecting a distribution change in any class, thus identifying which classes are affected by the concept drift. This can be precious information for diagnostics and adaptation. Our experiments on synthetic and real-world datastreams show that when the concept drift affects a few classes, CDM outperforms algorithms monitoring the overall data distribution, while achieving similar detection delays when the drift affects all the classes. Moreover, CDM outperforms comparable approaches that monitor the classification error, particularly when the change is not very apparent. Finally, we demonstrate that CDM inherits the properties of the underlying change detector, yielding an effective control over the expected time before a false alarm, or Average Run Length (ARL$_0$).
