The box-cox metric for nearest neighbour classification improvement

摘要

The Nearest Neighbour rule is a distribution-free classification method. In the literature it has been shown, however, that the performance of the method improves if complex, often locally defined metrics are chosen. In this paper we demonstrate that in many real world discrimination problems comparable or even better classification results can be obtained with a simple global metric based on the Box-Cox transformation. In three case studies this is demonstrated: synthetic data, the IRIS data and real radar data. In the latter experiment, a large reduction in classification error of more than a factor four is achieved. Copyright 1997 Pattern Recognition Society.