---
title: "Reduced Complexity Rule Induction"
authors:
  - "Sholom M. Weiss"
  - "Nitin Indurkhya"
doi: ""
url: "https://ijcai.org/proceedings/1991/99"
pdf: "https://ijcai.org/proceedings/1991/0099.pdf"
venue: "Proceedings of the Twelfth International Joint Conference on Artificial Intelligence"
year: 1991
pages: "678-683"
track: "Learning Be Knowledge Acquisition"
keywords:
---

# Reduced Complexity Rule Induction

Sholom M. Weiss, Nitin Indurkhya

*Proceedings of the Twelfth International Joint Conference on Artificial Intelligence*, pages 678-683.

## Abstract

We present an architecture for rule induction that emp~~izes COf!~pact, reduce~-complexity rules. A new heunsuc technique for findmg a covering rule set of sample data. is described. This technique refmes a set of producuon rules by iteratively replacing a component of a rule with its single best replacement. A meth<?<I for .rule in~uction has been developed that combmes th1s covenng and refinement scheme with other techniques known to help reduce the complexity of rule sets •. su~~ as weakest-link pruning, resampling, and the Judicious use of linear discriminants Published results on several real-world datasets arrevi~wed where d~ision trees have performed relauvely poorly. It 1S shown that far simpler decision rules can be found with predictive performance that exceeds .those previously reported for various learning models, mcluding neural nets and decision trees.