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Time-based detection of changes to multivariate patterns

11 years 5 months ago
Time-based detection of changes to multivariate patterns
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 probabilities ratios are derived from a relationship to likelihood ratios to form the basis for time-weighted updates of the monitoring scheme.
Jing Hu, George C. Runger
Added 08 Dec 2010
Updated 08 Dec 2010
Type Journal
Year 2010
Where ANOR
Authors Jing Hu, George C. Runger
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