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

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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.

Original languageEnglish
Title of host publication16th International Joint Conference on Artificial Intelligence Proceedings
Pages702-707
Number of pages6
Volume2
Publication statusPublished - 1 Dec 1999
Externally publishedYes
EventInternational Joint Conference on Artificial Intelligence 1999 - City Conference Center, Stockholm, Sweden
Duration: 31 Jul 19996 Aug 1999
Conference number: 16th
https://www.ijcai.org/proceedings/1999-1 (Conference Proceedings)
https://www.ijcai.org/past/ijcai-99/

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
ISSN (Print)1045-0823

Conference

ConferenceInternational Joint Conference on Artificial Intelligence 1999
Abbreviated titleIJCAI 1999
Country/TerritorySweden
CityStockholm
Period31/07/996/08/99
Internet address

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