---
title: "Learning Structural Decision Trees from Examples"
authors:
  - "Larry Watanabe"
  - "Larry A. Rendell"
doi: ""
url: "https://ijcai.org/proceedings/1991/113"
pdf: "https://ijcai.org/proceedings/1991/0113.pdf"
venue: "Proceedings of the Twelfth International Joint Conference on Artificial Intelligence"
year: 1991
pages: "770-775"
track: "Learning Be Knowledge Acquisition"
keywords:
---

# Learning Structural Decision Trees from Examples

Larry Watanabe, Larry A. Rendell

*Proceedings of the Twelfth International Joint Conference on Artificial Intelligence*, 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.