Abstract
The paper includes a description of a novel method for reducing the size of a training set in order to reduce memory requirements and classification complexity. Our method allows the condensing of the training set in a way that it is both training set consistent (classifies all training data points correctly) and decision-boundary consistent (the decision boundary does not changes after applying our method) for NN classifiers. Furthermore, the algorithm described here allows the utilization of a parallel computing paradigm in order to increase performance.
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Dwulit, M.P., Szymański, Z. (2012). Dwulit’s Hull as Means of Optimization of kNN Algorithm. In: Hippe, Z.S., Kulikowski, J.L., Mroczek, T. (eds) Human – Computer Systems Interaction: Backgrounds and Applications 2. Advances in Intelligent and Soft Computing, vol 99. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23172-8_23
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DOI: https://doi.org/10.1007/978-3-642-23172-8_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23171-1
Online ISBN: 978-3-642-23172-8
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