The Evolution of a Kernel-Based Distance Metric for k-NN Regression
Madden, Michael G. Howley, Tom
Madden, Michael G.
Howley, Tom
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2007
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Conference Paper
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The Evolution of a Kernel-Based Distance Metric for k-NN Regression , Tom Howley and Michael G. Madden. Proceedings of AICS-2007: 18th Irish Conference on Artificial Intelligence and Cognitive Science, Dublin, August 2007.
Abstract
k-Nearest Neighbours (k-NN) is a well understood and widely-used approach to classification and regression problems. In many cases, such applications of k-NN employ the standard Euclidean distance metric for the determination of the set of nearest neighbours to a particular test data sample. This paper investigates the use of a data-driven evolutionary approach, named KTree, for the automatic construction of a kernel-based distance metric as an alternative to Euclidean distance. The key idea behind this approach is that a different distance metric is generated for a particular data domain. The performance of k-NN with the standard Euclidean distance measure is compared with that of k-NN based on a kernel-based distance metric evolved by KTree. This comparison is based on experiments on both synthetic and real-world datasets.
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Attribution-NonCommercial-NoDerivs 3.0 Ireland