An Empirical Re-Examination of Weighted Voting for k-NN

For some applications of k-nearest neighbor classiiers, the best results are obtained at a relatively large value of k. With the majority voting method, these results can be suboptimal. In this paper the performance of various weighted voting methods is tested on a number of machine learning datasets. The results show that weighted voting is often superior to majority voting, and that the linear weighting function proposed by Dudani 5] often yields slightly better results than the inverse distance function that has commonly been used in more recent work.