Improving k-nearest neighbor density and error estimates

摘要

The subject of this paper is the estimation of the probability density function and probability of Bayes error using the k-nearest neighbor method. Modified formulas for density estimator are proposed, based on the analysis of the bias and the variance expressions. The modified density estimators are then used for Bayes error estimation. Performances of the newly proposed procedures are examined by executing controlled experiments on Gaussian data. The procedures exhibit slightly improved error estimates in the cases important for practical purposes.