---
title: "Oversearching and Layered Search in Empirical Learning"
authors:
  - "J. Ross Quinlan"
  - "R. Mike Cameron-Jones"
doi: ""
url: "https://ijcai.org/proceedings/1995/132"
pdf: "https://ijcai.org/proceedings/1995/0132.pdf"
venue: "Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence"
year: 1995
pages: "1019-1023"
track: "Learning"
keywords:
---

# Oversearching and Layered Search in Empirical Learning

J. Ross Quinlan, R. Mike Cameron-Jones

*Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence*, pages 1019-1023.

## Abstract

When learning classifiers, more extensive search for rules is shown to lead to lower predictive accuracy on many of the real-world domains investigated. This counter-intuitive result is particularly relevant to recent systematic search methods that use risk-free pruning to achieve the same outcome as exhaustive search. We propose an iterated search method that commences with greedy search, extending its scope at each iteration until a stopping criterion is satisfied. This layered search is often found to produce theories that are more accurate than those obtained with either greedy search or moderately extensive beam search.