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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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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DOI: https://doi.org/10.1007/978-3-540-87536-9_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87535-2
Online ISBN: 978-3-540-87536-9
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