On the performance of edited nearest neighbor rules in high dimensions

It is shown that, asymptotically, as the dimensionality of the space increases, the usual sample editing becomes independent. This makes an accurate calculation of performance in a high-dimensional space straightforward. Thus, with high dimensionality, the grouping given by J. Koplowitz and T.A. Brown (1981) is not necessary for determining the risk, and, similarly, the results presented by D.L. Wilson (1972) become very close to exact.