On the Reduction of the Nearest-Neighbor Variation for More Accurate Classification and Error Estimates

Authors:
Abdelhamid Djouadi
Affiliations:
Lucent Technologies, Columbus, OH
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
1998

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Abstract

In designing the nearest-neighbor (NN) classifier, a method is presented to produce a finite sample size risk close to the asymptotic one. It is based on an attempt to eliminate the first-order effects of the sample size, as well as all higher odd terms. This method uses the 2-NN rule without the rejection option and utilizes a polarization scheme. Simulation results are included as a means of verifying this analysis.