Detection of changes to multivariate patterns is an important topic in a number of different domains. Modern data sets often include categorical and numerical data and potentially complex in-control regions. Given a flexible, robust decision rule for this environment that signals based on an individual observation vector, an important issue is how to extend the rule to incorporate time-based information. A decision rule can be learned to detect shifts through artificial data that transforms the problem to one of supervised learning. Then class probability ratios are derived from a relationship to likelihood ratios to form the basis for time-weighted updates of the monitoring scheme.

Original languageEnglish (US)
Pages (from-to)67-81
Number of pages15
JournalAnnals of Operations Research
Issue number1
StatePublished - Feb 2010


  • Multivariate patterns
  • Supervised learning
  • Time-based detection of changes

ASJC Scopus subject areas

  • General Decision Sciences
  • Management Science and Operations Research


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