论文标题
基于自动回归的漂移检测方法
Autoregressive based Drift Detection Method
论文作者
论文摘要
在经典的机器学习框架中，对模型进行了对历史数据的培训，并用于预测未来的价值。假定数据分布不会随时间变化（平稳性）。但是，在实际情况下，数据生成过程会随着时间的推移而变化，并且该模型必须适应新的传入数据。这种现象称为概念漂移，导致预测模型的性能下降。在这项研究中，我们提出了一种基于称为ADDM的自回旋模型的新概念漂移检测方法。该方法可以集成到从深神经网络到简单线性回归模型的任何机器学习算法中。我们的结果表明，这种新概念漂移检测方法在合成数据集和现实世界数据集上都优于最先进的漂移检测方法。从理论上讲，我们的方法可以保证，并且可以进行经验和有效，可用于检测各种概念漂移。除了漂移检测器外，我们还根据漂移的严重程度提出了一种新的概念漂移适应方法。
In the classic machine learning framework, models are trained on historical data and used to predict future values. It is assumed that the data distribution does not change over time (stationarity). However, in real-world scenarios, the data generation process changes over time and the model has to adapt to the new incoming data. This phenomenon is known as concept drift and leads to a decrease in the predictive model's performance. In this study, we propose a new concept drift detection method based on autoregressive models called ADDM. This method can be integrated into any machine learning algorithm from deep neural networks to simple linear regression model. Our results show that this new concept drift detection method outperforms the state-of-the-art drift detection methods, both on synthetic data sets and real-world data sets. Our approach is theoretically guaranteed as well as empirical and effective for the detection of various concept drifts. In addition to the drift detector, we proposed a new method of concept drift adaptation based on the severity of the drift.
