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Paper Information

Journal:   JOURNAL OF SYSTEM MANAGEMENT   2019 , Volume 5 , Number 2; Page(s) 81 To 106.

Online Mean Shift Detection in Multivariate Quality Control Using Boosted Decision Tree learning

Author(s):  ASADI ABBAS, Farjami Yaghoub*
* Department of Computer and IT Engineering, University of Qom, Qom, Iran
The rapid development of communication technologies and information and online computers and their usage in processes of the industrial production have facilitated simultaneous monitoring of multiple variables (characteristics) in a process. In this work, we applied boosted decision tree          and Monte Carlo simulation to propose an efficient method for detecting incontrol and out-of-control states in multivariate control processes. In this work, four classifiers (methods)- ,    ,     ,   – are used for detecting the process control states. Then, with converting detection results these four classifiers, the boosted decision tree is made and provides the ultimate result as the incontrol or the out-of-control states. To show how the proposed model works and the superiority of this method over ,    ,     , and  methods, we run it on a standardized trivariate normal process. To compare and evaluate the performance of classifiers, we used ARL functions and the evaluation measures including Accuracy (ACC), Sensitivity (TPR), Specificity (SPC), and Precision (PPV). The findings not only showed the superiority of the proposed method over the tradition Chi-square but also confirmed former results on the efficiency of decision tree for rapid detecting of mean shifts in multivariate processes in which data are gathered automatically.
Keyword(s): Multivariate Quality Control,Mean Shift Detection,Boosted Decision Tree learning,Moving Window
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