Improving Performance of a Binary Classifier by Training Set Selection (original) (raw)

Abstract

In the paper a method of training set selection, in case of low data availability, is proposed and experimentally evaluated with the use of k-NN and neural classifiers. Application of proposed approach visibly improves the results compared to the case of training without postulated enhancements.

Moreover, a new measure of distance between events in the pattern space is proposed and tested with k-NN model. Numerical results are very promising and outperform the reference literature results of k-NN classifiers built with other distance measures.

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Authors and Affiliations

  1. Faculty of Mathematics and Information Science, Warsaw University of Technology, Plac Politechniki 1, 00-661, Warsaw, Poland
    Cezary Dendek & Jacek Mańdziuk

Authors

  1. Cezary Dendek
  2. Jacek Mańdziuk

Editor information

Véra Kůrková Roman Neruda Jan Koutník

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© 2008 Springer-Verlag Berlin Heidelberg

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Dendek, C., Mańdziuk, J. (2008). Improving Performance of a Binary Classifier by Training Set Selection. In: Kůrková, V., Neruda, R., Koutník, J. (eds) Artificial Neural Networks - ICANN 2008. ICANN 2008. Lecture Notes in Computer Science, vol 5163. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87536-9\_14

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