Knight, Brian and Woon, Fei Ling (2003) Case base adaptation using solution-space metrics. In: Proceedings of the 18th International Joint Conference on Artificial intelligence. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, pp. 1347-1348. ISBN 0127056610

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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 introducion of a solution-space metric is shown to out-perform conventional nearest neighbours methods on sparse case bases.

Item Type:	Conference Proceedings
Title of Proceedings:	Proceedings of the 18th International Joint Conference on Artificial intelligence
Additional Information:	[1] This poster was presented at the 18th International Joint Conference on Artificial Intelligence, (IJCAI-03), held from 9-15 August 2003 in Acapulco, Mexico.
Uncontrolled Keywords:	case-base reasoning
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Pre-2014 Departments:	School of Computing & Mathematical Sciences School of Computing & Mathematical Sciences > Computer & Computational Science Research Group School of Computing & Mathematical Sciences > Department of Computer Science
Related URLs:	Publisher Publisher Organisation Organisation
Last Modified:	14 Oct 2016 09:01
URI:	http://gala.gre.ac.uk/id/eprint/662

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