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Lookup NU author(s): Dr Kathryn Kipling,
Professor Gary Montague,
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This paper introduces a method for analysing correlated process data to extract useful information with multivariate decision trees. The techniques have been applied to a bioprocess data set and have been shown to provide insight into the causes of process bariation. The differences between the univariate and multivariate methods are highlighted and the tree interpretations are discussed. It is observed that there is very little difference in the classification abilities of the tree types from a prediction of the outcome perspective but the information from the multivariate trees is more informative with regard to cause of deviation rather than simply identifying the observed effects. © 2005 Elsevier B.V. All rights reserved.
Author(s): Kipling K, Montague G, Martin E, Morris J
Publication type: Article
Publication status: Published
Journal: Computer Aided Chemical Engineering
ISSN (print): 1570-7946
Publisher: Elsevier BV
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