Many methods have been proposed to detect concept drift, i.e., the change in the distribution of streaming data, due to concept drift causes a decrease in the prediction accuracy of algorithms. However, the most of current detection methods are based on the assessment of the degree of change in the data distribution, cannot identify the type of concept drift. In this paper, we propose Active Drift Detection with Meta learning (Meta-ADD), a novel framework that learns to classify concept drift by tracking the changed pattern of error rates. Specifically, in the training phase, we extract meta-features based on the error rates of various concept drift, after which a meta-detector is developed via a prototypical neural network by representing various concept drift classes as corresponding prototypes. In the detection phase, the learned meta-detector is fine-tuned to adapt to the corresponding data stream via stream-based active learning. Hence, Meta-ADD uses machine learning to learn to detect concept drifts and identify their types automatically, which can directly support drift understand. The experiment results verify the effectiveness of Meta-ADD.

中文翻译：

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自动学习以检测概念漂移

已经提出了许多方法来检测概念漂移，即，由于概念漂移导致算法的预测精度降低，从而导致流数据分布的变化。但是，当前大多数检测方法都是基于对数据分布变化程度的评估，无法识别概念漂移的类型。在本文中，我们提出了带有元学习的主动漂移检测（Meta-ADD），这是一个新颖的框架，可通过跟踪错误率的变化模式来学习对概念漂移进行分类。具体来说，在训练阶段，我们基于各种概念漂移的错误率提取元特征，然后通过原型神经网络将各种概念漂移类表示为相应的原型，从而开发出元检测器。在检测阶段，通过基于流的主动学习，对学习到的元检测器进行微调以适应相应的数据流。因此，Meta-ADD使用机器学习来学习检测概念漂移并自动识别它们的类型，从而可以直接支持对漂移的理解。实验结果证明了Meta-ADD的有效性。