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

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

13 Scopus Citations
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Author(s)

  • Thuntee Sukchotrat
  • Seoung Bum Kim
  • Kwok-Leung Tsui
  • Victoria C.P. Chen

Detail(s)

Original languageEnglish
Pages (from-to)1897-1911
Journal / PublicationJournal of Statistical Computation and Simulation
Volume81
Issue number12
Publication statusPublished - Dec 2011
Externally publishedYes

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. © 2011 Copyright Taylor and Francis Group, LLC.

Research Area(s)

  • data mining, Hotelling's T 2, multivariate statistical process control, supervised classification method

Citation Format(s)

Integration of classification algorithms and control chart techniques for monitoring multivariate processes. / Sukchotrat, Thuntee; Kim, Seoung Bum; Tsui, Kwok-Leung; Chen, Victoria C.P.

In: Journal of Statistical Computation and Simulation, Vol. 81, No. 12, 12.2011, p. 1897-1911.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review