Change-point detection with supervised learning and feature selection

Victor Eruhimov, Vladimir Martyanov, Eugene Tuv, George Runger

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish (US)
Title of host publicationICINCO 2007 - 4th International Conference on Informatics in Control, Automation and Robotics, Proceedings
Pages359-363
Number of pages5
VolumeICSO
StatePublished - 2007
Event4th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2007 - Angers, France
Duration: May 9 2007May 12 2007

Other

Other4th International Conference on Informatics in Control, Automation and Robotics, ICINCO 2007
CountryFrance
CityAngers
Period5/9/075/12/07

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

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  • Cite this

    Eruhimov, V., Martyanov, V., Tuv, E., & Runger, G. (2007). Change-point detection with supervised learning and feature selection. In ICINCO 2007 - 4th International Conference on Informatics in Control, Automation and Robotics, Proceedings (Vol. ICSO, pp. 359-363)