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 language | English |
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Title of host publication | 16th International Joint Conference on Artificial Intelligence Proceedings |
Pages | 702-707 |
Number of pages | 6 |
Volume | 2 |
Publication status | Published - 1 Dec 1999 |
Externally published | Yes |
Event | International Joint Conference on Artificial Intelligence 1999 - City Conference Center, Stockholm, Sweden Duration: 31 Jul 1999 → 6 Aug 1999 Conference number: 16th https://www.ijcai.org/proceedings/1999-1 (Conference Proceedings) https://www.ijcai.org/past/ijcai-99/ |
Publication series
Name | IJCAI International Joint Conference on Artificial Intelligence |
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ISSN (Print) | 1045-0823 |
Conference
Conference | International Joint Conference on Artificial Intelligence 1999 |
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Abbreviated title | IJCAI 1999 |
Country/Territory | Sweden |
City | Stockholm |
Period | 31/07/99 → 6/08/99 |
Internet address |
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