Nearest Neighbor Classiication with a Local Asymmetrically Weighted Metric

This paper introduces a new local asymmetric weighting scheme for the nearest neighbor classiication algorithm. It is shown both with theoretical arguments and computer experiments that good compression rates can be achieved outperforming the accuracy of the standard nearest neighbor classiication algorithm and obtaining almost the same accuracy as the k-NN algorithm with k optimised in each data set. The improvement in time performance is proportional to the compression rate and in general it depends on the data set. The comparison of the classiication accuracy of the proposed algorithm with a local symmetrically weighted metric and with a global metric strongly shows that the proposed scheme is to be preferred.