Learning Structural Decision Trees from Examples

Watanabe, Larry; Rendell, Larry A.

Learning Structural Decision Trees from Examples

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Proceedings of the Twelfth International Joint Conference on Artificial Intelligence. Learning Be Knowledge Acquisition. Pages 770-775.

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.

Larry Watanabe, and Larry A. Rendell. 1991. Learning Structural Decision Trees from Examples. In Proceedings of the Twelfth International Joint Conference on Artificial Intelligence, IJCAI-91. IJCAI, 770–775. https://ijcai.org/proceedings/1991/113

@inproceedings{ijcai1991p113,
  title     = {Learning Structural Decision Trees from Examples},
  author    = {Watanabe, Larry and Rendell, Larry A.},
  booktitle = {Proceedings of the Twelfth International Joint Conference on
               Artificial Intelligence, {IJCAI-91}},
  publisher = {International Joint Conferences on Artificial Intelligence},
  editor    = {John Mylopoulos and Ray Reiter},
  pages     = {770--775},
  year      = {1991},
  month     = {8},
  note      = {Learning Be Knowledge Acquisition},
  url       = {https://ijcai.org/proceedings/1991/113},
}

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TI  - Learning Structural Decision Trees from Examples
AU  - Watanabe, Larry
AU  - Rendell, Larry A.
T2  - Proceedings of the Twelfth International Joint Conference on Artificial Intelligence, IJCAI-91
PB  - International Joint Conferences on Artificial Intelligence
PY  - 1991
SP  - 770
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UR  - https://ijcai.org/proceedings/1991/113
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