---
title: "Induction of Oblique Decision Trees"
authors:
  - "David G. Heath"
  - "Simon Kasif"
  - "Steven Salzberg"
doi: ""
url: "https://ijcai.org/proceedings/1993/138"
pdf: "https://ijcai.org/proceedings/1993/0138.pdf"
venue: "Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence"
year: 1993
pages: "1002-1006"
track: "Machine Learning"
keywords:
---

# Induction of Oblique Decision Trees

David G. Heath, Simon Kasif, Steven Salzberg

*Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence*, pages 1002-1006.

## Abstract

This paper introduces a randomized technique for partitioning examples using oblique hyperplanes. Standard decision tree techniques, such as ID3 and its descendants, partition a set of points with axis-parallel hyperplanes. Our method, by contrast, attempts to find hyperplanes at any orientation. The purpose of this more general technique is to find smaller but equally accurate decision trees than those created by other methods. We have tested our algorithm on both real and simulated data, and found that in some cases it produces surprisingly small trees without losing predictive accuracy. Small trees allow us, in turn, to obtain simple qualitative descriptions of each problem domain.