Abstract
One-class classification problems have attracted a great deal of attention from various disciplines. In the present study, attempts are made to extend the scope of application of the one-class classification technique to Statistical Process Control (SPC) problems. New multivariate control charts that apply the effectiveness of one-class classification to improvement of Phase I and Phase II analysis in SPC are proposed. These charts use a monitoring statistic to represent the degree of being an outlier as obtained through one-class classification. The control limits of the proposed charts are established based on the empirical level of significance on the percentile, estimated by the bootstrap method. A simulation study is conducted to illustrate the limitations of current one-class classification control charts and demonstrate the effectiveness of the proposed control charts.
| Original language | English |
|---|---|
| Pages (from-to) | 107-120 |
| Number of pages | 14 |
| Journal | IIE Transactions (Institute of Industrial Engineers) |
| Volume | 42 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 2010 Feb 1 |
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Keywords
- Data mining
- Hotelling's T
- Multivariate process
- One-class classification method
- Statistical process control
ASJC Scopus subject areas
- Industrial and Manufacturing Engineering
Cite this
One-class classification-based control charts for multivariate process monitoring. / Sukchotrat, Thuntee; Kim, Seoung Bum; Tsung, Fugee.
In: IIE Transactions (Institute of Industrial Engineers), Vol. 42, No. 2, 01.02.2010, p. 107-120.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - One-class classification-based control charts for multivariate process monitoring
AU - Sukchotrat, Thuntee
AU - Kim, Seoung Bum
AU - Tsung, Fugee
PY - 2010/2/1
Y1 - 2010/2/1
N2 - One-class classification problems have attracted a great deal of attention from various disciplines. In the present study, attempts are made to extend the scope of application of the one-class classification technique to Statistical Process Control (SPC) problems. New multivariate control charts that apply the effectiveness of one-class classification to improvement of Phase I and Phase II analysis in SPC are proposed. These charts use a monitoring statistic to represent the degree of being an outlier as obtained through one-class classification. The control limits of the proposed charts are established based on the empirical level of significance on the percentile, estimated by the bootstrap method. A simulation study is conducted to illustrate the limitations of current one-class classification control charts and demonstrate the effectiveness of the proposed control charts.
AB - One-class classification problems have attracted a great deal of attention from various disciplines. In the present study, attempts are made to extend the scope of application of the one-class classification technique to Statistical Process Control (SPC) problems. New multivariate control charts that apply the effectiveness of one-class classification to improvement of Phase I and Phase II analysis in SPC are proposed. These charts use a monitoring statistic to represent the degree of being an outlier as obtained through one-class classification. The control limits of the proposed charts are established based on the empirical level of significance on the percentile, estimated by the bootstrap method. A simulation study is conducted to illustrate the limitations of current one-class classification control charts and demonstrate the effectiveness of the proposed control charts.
KW - Data mining
KW - Hotelling's T
KW - Multivariate process
KW - One-class classification method
KW - Statistical process control
UR - http://www.scopus.com/inward/record.url?scp=77649328220&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77649328220&partnerID=8YFLogxK
U2 - 10.1080/07408170903019150
DO - 10.1080/07408170903019150
M3 - Article
AN - SCOPUS:77649328220
VL - 42
SP - 107
EP - 120
JO - IISE Transactions
JF - IISE Transactions
SN - 2472-5854
IS - 2
ER -