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

Thuntee Sukchotrat, Seoung Bum Kim, Kwok Leung Tsui, Victoria C P Chen

Research output: Contribution to journalArticle

13 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1897-1911
Number of pages15
JournalJournal of Statistical Computation and Simulation
Issue number12
Publication statusPublished - 2011 Dec 1



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

ASJC Scopus subject areas

  • Applied Mathematics
  • Statistics and Probability
  • Modelling and Simulation
  • Statistics, Probability and Uncertainty

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