---
title: "Learning DNF by Decision Trees"
authors:
  - "Giulia Pagallo"
doi: ""
url: "https://ijcai.org/proceedings/1989/102"
pdf: "https://ijcai.org/proceedings/1989/0102.pdf"
venue: "Proceedings of the Eleventh International Joint Conference on Artificial Intelligence"
year: 1989
pages: "639-643"
track: "Machine Learning"
keywords:
---

# Learning DNF by Decision Trees

Giulia Pagallo

*Proceedings of the Eleventh International Joint Conference on Artificial Intelligence*, pages 639-643.

## Abstract

We investigate the problem of learning DNF concepts from examples using decision trees as a concept description language. Due to the replication problem, DNF concepts do not always have a concise decision tree description when the tests at the nodes are limited to the initial attributes . However, the representational complexity may be overcome by using high level attributes as tests. We present a novel algorithm that modifies the initial bias determined by the primitive attributes by adaptively enlarging the attribute set with high level attributes. We show empirically that this algorithm outperforms a standard decision tree algorithm for learning small random DNF with and without noise, when the examples are drawn from the uniform distribution.