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In this tutorial, traditional decision tree construction and the current state of decision tree modeling are reviewed. Emphasis is placed on techniques that make decision trees well suited to handle the complexities of chemical and biochemical applications.

Analogous to the situation found in calibration, a classification model constructed from spectra measured on one instrument may not be valid for prediction of class from spectra measured on a second instrument. In this paper, the transfer of multivariate classification models between laboratory and process near-infrared spectrometers is investigated for the discrimination of whole, green Coffea arabica (Arabica) and Coffea canefora (Robusta) coffee beans. A modified version of slope/bias correction, orthogonal signal correction trained on a vector of discrete class identities, and model updating were found to perform well in the preprocessing of data to permit the transfer of a classification model developed on data from one instrument to be used on another instrument. These techniques permitted development of robust models for the discrimination of green coffee beans on both spectrometers and resulted in misclassification errors for the transfer process in the range of 5-10%.

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A new method for the induction of fuzzy decision trees is introduced. The fuzzy decision tree classifier improves prediction accuracy using smaller models by locating more robust splitting regions. The proposed method also provides a measure of confidence for sample classification by propagating partition memberships into all leaf nodes, thereby relaxing local subspace restrictions. The fuzzy decision tree algorithm is presented and compared against standard and bagged decision tree classifiers.