Abstract
Data streams with high dimensions are more and more common as data sets become wider. Time segments of stable system performance are often interrupted with change events. The change-point problem is to detect such changes and identify attributes that contribute to the change. Existing methods focus on detecting a single (or few) change-point in a univariate (or low-dimensional) process. We consider the important highdimensional multivariate case with multiple change-points and without an assumed distribution. The problem is transformed to a supervised learning problem with time as the output response and the process variables as inputs. This opens the problem to a wide set of supervised learning tools. Feature selection methods are used to identify the subset of variables that change. An illustrative example illustrates the method in an important type of application.
| Original language | English (US) |
|---|---|
| Title of host publication | ICINCO 2007 - 4th International Conference on Informatics in Control, Automation and Robotics, Proceedings |
| Pages | 359-363 |
| Number of pages | 5 |
| Volume | ICSO |
| State | Published - 2007 |
| Event | 4th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2007 - Angers, France Duration: May 9 2007 → May 12 2007 |
Other
| Other | 4th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2007 |
|---|---|
| Country | France |
| City | Angers |
| Period | 5/9/07 → 5/12/07 |
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Keywords
- Data streams
- Ensembles
- Multivariate control
- Variable importance
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Science Applications
- Human-Computer Interaction
- Software
- Control and Systems Engineering
- Electrical and Electronic Engineering
Cite this
Change-point detection with supervised learning and feature selection. / Eruhimov, Victor; Martyanov, Vladimir; Tuv, Eugene; Runger, George.
ICINCO 2007 - 4th International Conference on Informatics in Control, Automation and Robotics, Proceedings. Vol. ICSO 2007. p. 359-363.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Change-point detection with supervised learning and feature selection
AU - Eruhimov, Victor
AU - Martyanov, Vladimir
AU - Tuv, Eugene
AU - Runger, George
PY - 2007
Y1 - 2007
N2 - Data streams with high dimensions are more and more common as data sets become wider. Time segments of stable system performance are often interrupted with change events. The change-point problem is to detect such changes and identify attributes that contribute to the change. Existing methods focus on detecting a single (or few) change-point in a univariate (or low-dimensional) process. We consider the important highdimensional multivariate case with multiple change-points and without an assumed distribution. The problem is transformed to a supervised learning problem with time as the output response and the process variables as inputs. This opens the problem to a wide set of supervised learning tools. Feature selection methods are used to identify the subset of variables that change. An illustrative example illustrates the method in an important type of application.
AB - Data streams with high dimensions are more and more common as data sets become wider. Time segments of stable system performance are often interrupted with change events. The change-point problem is to detect such changes and identify attributes that contribute to the change. Existing methods focus on detecting a single (or few) change-point in a univariate (or low-dimensional) process. We consider the important highdimensional multivariate case with multiple change-points and without an assumed distribution. The problem is transformed to a supervised learning problem with time as the output response and the process variables as inputs. This opens the problem to a wide set of supervised learning tools. Feature selection methods are used to identify the subset of variables that change. An illustrative example illustrates the method in an important type of application.
KW - Data streams
KW - Ensembles
KW - Multivariate control
KW - Variable importance
UR - http://www.scopus.com/inward/record.url?scp=67149136281&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=67149136281&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:67149136281
VL - ICSO
SP - 359
EP - 363
BT - ICINCO 2007 - 4th International Conference on Informatics in Control, Automation and Robotics, Proceedings
ER -