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Integration of classification algorithms and control chart techniques for monitoring multivariate processes

Authors
Sukchotrat, ThunteeKim, Seoung BumTsui, Kwok-LeungChen, Victoria C. P.
Issue Date
2011
Publisher
TAYLOR & FRANCIS LTD
Keywords
data mining; Hotelling' s T-2; multivariate statistical process control; supervised classification method
Citation
JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, v.81, no.12, pp.1897 - 1911
Indexed
SCIE
SCOPUS
Journal Title
JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION
Volume
81
Number
12
Start Page
1897
End Page
1911
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/114878
DOI
10.1080/00949655.2010.507765
ISSN
0094-9655
Abstract
We propose new multivariate control charts that can effectively deal with massive amounts of complex data through their integration with classification algorithms. We call the proposed control chart the 'Probability of Class (PoC) chart' because the values of PoC, obtained from classification algorithms, are used as monitoring statistics. The control limits of PoC charts are established and adjusted by the bootstrap method. Experimental results with simulated and real data showed that PoC charts outperform Hotelling's T-2 control charts. Further, a simulation study revealed that a small proportion of out-of-control observations are sufficient for PoC charts to achieve the desired performance.
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