Combination of Several Control Charts Based on Dynamic Ensemble Methods

Abstract

Combining methods from Statistical Process Control (SPC) in order to benefit from more than one method's efficiency has been recently challenged. One of the reasons is that real life problems change overtime and a small improvement can lead to a very big profit. Ensemble methods from data mining domain have recently shown their effectiveness when used with SPC. The first combined control chart based on dynamic ensemble method, called Dynamic weighted control chart, is designed especially for monitoring concept drift in online processes. This article presents a new model of combining more than two control charts based on ensemble methods as well as error rates classifications to optimize the shift identification and control. This method can be applied for offline and online processes. It is based on a three step learning model: first a preprocessing step to prepare the data for classification. Second, an ensemble method based on Dynamic Weighted Majority (DWM) is applied to aggregate the decisions of the different charts at the end of the each batch. Finally, shifts are identified based on the misclassification error rates of DWM. Dynamic Ensemble Control chart model benefits from the knowledge from classification and control to give a most precise information about the process. Experiments have shown that the latter is better than the use of individual charts and classifies the variable which is responsible for the out of control.