A new dynamic weighted majority control chart for data streams

Abstract

Dynamics are fundamental properties of batch learning processes. Recently, monitoring dynamic processes has interested many researchers due to the importance of dealing with time-changing data stream processes in real-world applications. In this article, a dynamic weighted majority (DWM)-based identification model is proposed for monitoring small, large as well as covariate shifts in nonstationary processes. The proposed method applies DWM ensemble method to aggregate decisions of different control charts to improve single charts’ performances and to reduce the risk of choosing a nonadequate chart. Also in order to improve the shift adaptation mode, a prediction of class label is used to help in classifying the shift during the changing of the process toward the approximated right direction. The new proposed ensemble chart has the ability to deal with complex datasets and presents a concrete shift identification method based on a classification learning technique of changes in nonstationary processes.