---
title: "Constructive Induction On Decision Trees"
authors:
  - "Christopher J. Matheus"
  - "Larry A. Rendell"
doi: ""
url: "https://ijcai.org/proceedings/1989/103"
pdf: "https://ijcai.org/proceedings/1989/0103.pdf"
venue: "Proceedings of the Eleventh International Joint Conference on Artificial Intelligence"
year: 1989
pages: "645-649"
track: "Machine Learning"
keywords:
---

# Constructive Induction On Decision Trees

Christopher J. Matheus, Larry A. Rendell

*Proceedings of the Eleventh International Joint Conference on Artificial Intelligence*, pages 645-649.

## Abstract

Selective induction techniques perform poorly when the features are inappropriate for the target concept. One solution is to have the learning system construct new features automatically; unfortunately feature construction is a difficult and poorly understood problem . In this paper we present a definition of feature construction in concept learning, and offer a framework for its study based on four aspects: detection, selection, generalization, and evaluation. This framework is used in the analysis of existing learning systems and as the basis for the design of a new system, CITRE. CITRE performs feature construction using decision trees and simple domain knowledge as constructive biases. Initial results on a set of spatial-dependent problems suggest the importance of domain knowledge and feature generalization, i.e., constructive induction.