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Probability-Based Distance Function for Distance-Based Classifiers

  • Cezary Dendek
  • Jacek Mańdziuk
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5768)

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.

Keywords

Distance Measure Cumulative Density Function Pattern Space Outlier Removal Probabilistic Distance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Cezary Dendek
    • 1
  • Jacek Mańdziuk
    • 1
  1. 1.Faculty of Mathematics and Information ScienceWarsaw University of TechnologyWarsawPoland

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