Efficient prototype reordering in nearest neighbor classification

Abstract

Nearest Neighbor rule is one of the most commonly used supervised classification procedures due to its inherent simplicity and intuitive appeal. However, it suffers from the major limitation of requiring n distance computations, where n is the size of the training data (or prototypes), for computing the nearest neighbor of a point. In this paper we suggest a simple approach based on rearrangement of the training data set in a certain order, such that the number of distance computations is significantly reduced. At the same time, the classification accuracy of the original rule remains unaffected. This method requires the storage of at most n distances in addition to the prototypes. The superiority of the proposed method in comparison to some other methods is clearly established in terms of the number of distances computed, the time required for finding the nearest neighbor, number of optimized operations required in the overhead computation and memory requirements. Variation of the performance of the proposed method with the size of the test data is also demonstrated.