---
title: "Case Base Adaptation Using Solution-Space Metrics"
authors:
  - "Brian Knight"
  - "Fei Ling Woon"
doi: ""
url: "https://ijcai.org/proceedings/2003/193"
pdf: "https://ijcai.org/proceedings/2003/0193.pdf"
venue: "Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence"
year: 2003
pages: "1347-1347"
track: ""
keywords:
---

# Case Base Adaptation Using Solution-Space Metrics

Brian Knight, Fei Ling Woon

*Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence*, pages 1347-1347.

## Abstract

In this paper we propose a generalisation of the k-nearest neighbour (k-NN) retrieval method based on an error function using distance metrics in the solution and problem space. It is an interpolative method which is proposed to be effective for sparse case bases. The method applies equally to nominal, continuous and mixed domains, and does not depend upon an embedding n-dimensional space. In continuous Euclidean problem domains, the method is shown to be a generalisation of the Shepard's Interpolation method. We term the retrieval algorithm the Generalised Shepard Nearest Neighbour (GSNN) method. A novel aspect of GSNN is that it provides a general method for interpolation over nominal solution domains. The performance of the retrieval method is examined with reference to the Iris classification problem, and to a simulated sparse nominal value test problem. The introduction of a solution-space metric is shown to out-perform conventional nearest neighbours methods on sparse case bases.