联合样本输出与特征空间的半监督概念漂移检测法及其应用

Semi-supervised Concept Drift Detection Method by Combining Sample Output Space and Feature Space With Its Application

城市固废焚烧(Municipal solid waste incineration,MSWI)过程受垃圾成分波动、设备磨损与维修、季节交替变化等因素的影响而存在概念漂移现象,这导致用于污染物排放浓度的建模数据具有时变性.为此,需要识别能够表征概念漂移的新样本对污染物测量模型进行更新,但现有漂移检测方法难以有效应用于建模样本真值获取困难的工业过程.针对上述问题,提出一种联合样本输出与特征空间的半监督概念漂移检测方法.首先,采用基于主成分分析(Principal component analysis,PCA)的无监督机制识别特征空间内的概念漂移样本;然后,在样本输出空间采用基于时间差分(Temporaldifference,TD)学习的半监督机制对上述概念漂移样本进行伪真值标注后,再用Page-Hinkley检测法确认能够表征概念漂移的样本;最后,采用上述步骤获得的新样本结合历史样本对模型进行更新.基于合成和真实工业过程数据集的仿真结果表明所提方法具有优于已有方法的性能,能够在加强模型漂移适应性的同时有效缩减样本标注成本.

The modeling data used for pollutant emission concentration in the municipal solid waste incineration(MSWI)is time-varying due to the concept drift phenomenon,which is caused by factors such as fluctuations in waste composition,equipment wear and repair,and seasonal changes.Thus,it is necessary to identify new samples that can represent the concept drift for pollutant measurement model updating.However,the existing methods are limited by the modeling samples’true values,which are difficult to be effectively applied to industrial processes.Thus,a semi-supervised concept drift detection method by combining sample output space and feature space is proposed.Firstly,unsupervised mechanism based on principal component analysis(PCA)is used in the sample feature space to identify concept drift samples.Then,semi-supervised mechanism based on temporal-difference(TD)learning is used in the sample output space to label the pseudo-true value for the identified concept drift samples.Further,the Page-Hinkley detection method is used to confirm the concept drift samples.Finally,the new samples obtained by the above steps are combined with historical samples to update the measurement model.The simulation results based on synthetic and real industrial process data sets show that the proposed method has better performance than the existing methods.Moreover,the cost of sample annotation is effectively reduced and the drift adaptability of the measurement model is enhanced.