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Integration of support vector machines and control charts for multivariate process monitoring

Authors
Chongfuangprinya, PanitarnKim, Seoung BumPark, Sun-KyoungSukchotrat, Thuntee
Issue Date
2011
Publisher
TAYLOR & FRANCIS LTD
Keywords
bootstrap; data mining; multivariate control charts; statistical quality control; support vector machines
Citation
JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, v.81, no.9, pp.1157 - 1173
Indexed
SCIE
SCOPUS
Journal Title
JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION
Volume
81
Number
9
Start Page
1157
End Page
1173
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/114968
DOI
10.1080/00949651003789074
ISSN
0094-9655
Abstract
Statistical process control tools have been used routinely to improve process capabilities through reliable on-line monitoring and diagnostic processes. In the present paper, we propose a novel multivariate control chart that integrates a support vector machine (SVM) algorithm, a bootstrap method, and a control chart technique to improve multivariate process monitoring. The proposed chart uses as the monitoring statistic the predicted probability of class (PoC) values from an SVM algorithm. The control limits of SVM-PoC charts are obtained by a bootstrap approach. A simulation study was conducted to evaluate the performance of the proposed SVM-PoC chart and to compare it with other data mining-based control charts and Hotelling's T-2 control charts under various scenarios. The results showed that the proposed SVM-PoC charts outperformed other multivariate control charts in nonnormal situations. Further, we developed an exponential weighed moving average version of the SVM-PoC charts for increasing sensitivity to small shifts.
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