Decision Tree Grafting From the All Tests But One Partition
Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence. Machine Learning. Pages 702-706.
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.
@inproceedings{ijcai1999p100,
title = {Decision Tree Grafting From the All Tests But One Partition},
author = {Webb, Geoffrey I.},
booktitle = {Proceedings of the Sixteenth International Joint Conference on
Artificial Intelligence, {IJCAI-99}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Thomas Dean},
pages = {702--706},
year = {1999},
note = {Machine Learning},
url = {https://ijcai.org/proceedings/1999/100},
}
TY - CPAPER TI - Decision Tree Grafting From the All Tests But One Partition AU - Webb, Geoffrey I. T2 - Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, IJCAI-99 PB - International Joint Conferences on Artificial Intelligence PY - 1999 SP - 702 EP - 706 UR - https://ijcai.org/proceedings/1999/100 ER -Copied
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