Decision tree grafting from the all-tests-but-one partition

Authors:
Geoffrey I. Webb
Affiliations:
School of Computing and Mathematics, Deakin University, Geelong, Vic., Australia
Venue:
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Year:
1999

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Abstract

Decision tree grafting adds nodes to an existing decision tree with the objective of reducing prediction error. A new grafting algorithm is presented that considers one set of training data only for each leaf of the initial decision tree, the set of cases that fail at most one test on the path to the leaf. This new technique is demonstrated to retain the error reduction power of the original grafting algorithm while dramatically reducing compute time and the complexity of the inferred tree. Bias/variance analyses reveal that the original grafting technique operated primarily by variance reduction while the new technique reduces both bias and variance.