One-class classification techniques for improving phase I analysis in statistical process control

Thuntee Sukchotrat, Seoung Bum Kim

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

Abstract

We propose to use a one-class classification technique to improve phase I analysis in statistical process control. Phase I analysis attempts to isolate the in-control data from the historical data, usually unlabelled to construct the reliable control charts. A traditional phase I method recursively removes the observations, which exceed the control limits until no out-of-control observations are detected. This recursive method requires a distributional assumption because the control limits are determined based on a certain parametric distribution. The proposed oneclassification technique does not require such distributional assumption. The effectiveness of the proposed approach is demonstrated through the simulated data set.

Original languageEnglish
Title of host publicationIIE Annual Conference and Expo 2008
Pages846-850
Number of pages5
Publication statusPublished - 2008 Dec 1
Externally publishedYes
EventIIE Annual Conference and Expo 2008 - Vancouver, BC, Canada
Duration: 2008 May 172008 May 21

Other

OtherIIE Annual Conference and Expo 2008
CountryCanada
CityVancouver, BC
Period08/5/1708/5/21

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Keywords

  • Data mining
  • Multivariate statistical process control
  • One-class classification
  • Phase I analysis

ASJC Scopus subject areas

  • Computer Science Applications
  • Software
  • Industrial and Manufacturing Engineering

Cite this

Sukchotrat, T., & Kim, S. B. (2008). One-class classification techniques for improving phase I analysis in statistical process control. In IIE Annual Conference and Expo 2008 (pp. 846-850)