Abstract
The detection of changes in the distribution of process variables is referred to as the change-point problem. Existing methods focus on detecting a single (or few) change point in a univariate (or low-dimensional) process. We consider the important high-dimensional multivariate case with multiple change points and without an assumed distribution. In this work the problem is transformed into a supervised learning problem with time as the output response and the process variables as inputs. Our focus is to identify the subset of variables that change. This important, practical scenario is analysed through a supervised learner with a variable importance measure that is used to identify the variables that change among hundreds of variables. Simulated cases are discussed in the paper to verify the proposed method. Moreover, the same data sets are compared with a multivariate exponentially weighted moving average control chart and the advantages of the supervised learner are illustrated.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 2853-2868 |
| Number of pages | 16 |
| Journal | International Journal of Production Research |
| Volume | 44 |
| Issue number | 14 |
| DOIs | |
| State | Published - Jul 15 2006 |
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Keywords
- Change point
- Decision tree
- Out-of-bag
- Randomforest
- Statistical process control
- Variable importance
ASJC Scopus subject areas
- Industrial and Manufacturing Engineering
- Management Science and Operations Research
Cite this
Supervised learning for change-point detection. / Li, Fang; Runger, George; Tuv, Eugene.
In: International Journal of Production Research, Vol. 44, No. 14, 15.07.2006, p. 2853-2868.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Supervised learning for change-point detection
AU - Li, Fang
AU - Runger, George
AU - Tuv, Eugene
PY - 2006/7/15
Y1 - 2006/7/15
N2 - The detection of changes in the distribution of process variables is referred to as the change-point problem. Existing methods focus on detecting a single (or few) change point in a univariate (or low-dimensional) process. We consider the important high-dimensional multivariate case with multiple change points and without an assumed distribution. In this work the problem is transformed into a supervised learning problem with time as the output response and the process variables as inputs. Our focus is to identify the subset of variables that change. This important, practical scenario is analysed through a supervised learner with a variable importance measure that is used to identify the variables that change among hundreds of variables. Simulated cases are discussed in the paper to verify the proposed method. Moreover, the same data sets are compared with a multivariate exponentially weighted moving average control chart and the advantages of the supervised learner are illustrated.
AB - The detection of changes in the distribution of process variables is referred to as the change-point problem. Existing methods focus on detecting a single (or few) change point in a univariate (or low-dimensional) process. We consider the important high-dimensional multivariate case with multiple change points and without an assumed distribution. In this work the problem is transformed into a supervised learning problem with time as the output response and the process variables as inputs. Our focus is to identify the subset of variables that change. This important, practical scenario is analysed through a supervised learner with a variable importance measure that is used to identify the variables that change among hundreds of variables. Simulated cases are discussed in the paper to verify the proposed method. Moreover, the same data sets are compared with a multivariate exponentially weighted moving average control chart and the advantages of the supervised learner are illustrated.
KW - Change point
KW - Decision tree
KW - Out-of-bag
KW - Randomforest
KW - Statistical process control
KW - Variable importance
UR - http://www.scopus.com/inward/record.url?scp=33744977241&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33744977241&partnerID=8YFLogxK
U2 - 10.1080/00207540600669846
DO - 10.1080/00207540600669846
M3 - Article
AN - SCOPUS:33744977241
VL - 44
SP - 2853
EP - 2868
JO - International Journal of Production Research
JF - International Journal of Production Research
SN - 0020-7543
IS - 14
ER -