The multi-class metric problem in nearest neighbour discrimination rules

Nearest neighbour rules classify a new data point on the basis of the class of the closest design set point. A prerequisite for this is a choice of metric by which distance may be calculated. Metrics which have been suggested for the two-class case fall into two classes: global and local. In this pa...

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Bibliographic Details
Published in:Pattern recognition 1990, Vol.23 (11), p.1291-1297
Main Authors: Myles, J.P., Hand, D.J.
Format: Article
Language:English
Subjects:
Discrimination
Metric
Multi-class
Nearest neighbour method
Simulation
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Summary:Nearest neighbour rules classify a new data point on the basis of the class of the closest design set point. A prerequisite for this is a choice of metric by which distance may be calculated. Metrics which have been suggested for the two-class case fall into two classes: global and local. In this paper local metrics are described and are generalized to the multi-class case. Properties of various possible generalizations are compared, and the results of a simulation study are described.
ISSN:0031-3203
1873-5142
DOI:10.1016/0031-3203(90)90123-3