Learning Structural Decision Trees from Examples
Abstract
STRUCT is a system that learns structural decision trees from positive and negative examples. The algorithm uses a modification of Pagallo and Haussler's FRINGE algorithm to construct new features in a first-order representation. Experiments compare the effects of different hypothesis evaluation strategies, domain representation, and feature construction. STRUCT is also compared with Quinlan's FOIL on two domains. The results show that a modified FRINGE algorithm improves accuracy, but that it is sensitive to the distribution of the examples.
Cite
Text
Watanabe and Rendell. "Learning Structural Decision Trees from Examples." International Joint Conference on Artificial Intelligence, 1991.Markdown
[Watanabe and Rendell. "Learning Structural Decision Trees from Examples." International Joint Conference on Artificial Intelligence, 1991.](https://mlanthology.org/ijcai/1991/watanabe1991ijcai-learning/)BibTeX
@inproceedings{watanabe1991ijcai-learning,
title = {{Learning Structural Decision Trees from Examples}},
author = {Watanabe, Larry and Rendell, Larry A.},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {1991},
pages = {770-776},
url = {https://mlanthology.org/ijcai/1991/watanabe1991ijcai-learning/}
}