Probability-Based Distance Function for Distance-Based Classifiers

Authors:
Cezary Dendek;Jacek Mańdziuk
Affiliations:
Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland 00-661;Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland 00-661
Venue:
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Year:
2009

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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.