Abstract

This paper introduces a new local asymmetric weighting scheme for the nearest neighbor classification 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 classification 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 classification 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. 1 Introduction Nearest neighbor algorithms (NN) are ubiquitous in many research areas such as pattern recognition, machine learning and case based reasoning. The k-NN algorithm, a simple generalisation of NN which is 1-NN, maintains a set of training examples and classif...

Citations