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Multivariate decision trees for the interrogation of bioprocess data

DOI: 10.1016/s1570-7946(05)80030-6
  • Multivariate Decision Trees
  • Correlated Data
  • Bioprocess Data


Abstract 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.

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