Probability-based distance function for distance-based classifiers

Abstract

In the paper a new measure of distance between events/observations in the pattern space is proposed and experimentally evaluated with the use of k-NN classifier in the context of binary classification problems. The application of the proposed approach visibly improves the results compared to the case of training without postulated enhancements in terms of speed and accuracy. Numerical results are very promising and outperform the reference literature results of k-NN classifiers built with other distance measures. © 2009 Springer Berlin Heidelberg.