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.
翻译:提出了许多方法来探测概念漂移,即流数据分布的变化,因为概念漂移导致流数据分布的变化,导致算法预测准确性下降;然而,目前大多数探测方法都基于对数据分布变化程度的评估,无法确定概念漂移的类型;在本文件中,我们提议采用Meta学习(Meta-ADD)来积极钻探,这是一个新颖的框架,通过跟踪错误率的变化模式来对概念漂移进行分类。具体地说,在培训阶段,我们根据各种概念漂移的错误率提取元元特点,然后通过一个原型神经网络,通过将各种概念漂移类别作为相应的原型来开发元指标。在探测阶段,所学的元数据探测器经过微调,通过以流为基础的积极学习来适应相应的数据流。因此,Meta-ADDD利用机器学习来学习如何探测概念漂移并自动识别其类型,从而直接支持漂移。实验结果对Meta-ADDD的有效性进行了核查。