On the relation of performance to editing in nearest neighbor rules

摘要

A general class of editing schemes is examined which allows for the relabeling as well as the deletion of samples. It is shown that there is a trade-off between asymptotic performance and sample deletion which can adversely affect the finite sample performance. A kk′ rule is proposed to minimize the proportion of deleted samples. A slight modification of the rule is introduced which allows for an exact analysis in any dimension.