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Identifying predictive hubs to condense the training set of $$k$$ -nearest neighbour classifiers

Author

Listed:
  • Ludwig Lausser
  • Christoph Müssel
  • Alexander Melkozerov
  • Hans Kestler

Abstract

The $$k$$ -Nearest Neighbour classifier is widely used and popular due to its inherent simplicity and the avoidance of model assumptions. Although the approach has been shown to yield a near-optimal classification performance for an infinite number of samples, a selection of the most decisive data points can improve the classification accuracy considerably in real settings with a limited number of samples. At the same time, a selection of a subset of representative training samples reduces the required amount of storage and computational resources. We devised a new approach that selects a representative training subset on the basis of an evolutionary optimization procedure. This method chooses those training samples that have a strong influence on the correct prediction of other training samples, in particular those that have uncertain labels. The performance of the algorithm is evaluated on different data sets. Additionally, we provide graphical examples of the selection procedure. Copyright Springer-Verlag Berlin Heidelberg 2014

Suggested Citation

  • Ludwig Lausser & Christoph Müssel & Alexander Melkozerov & Hans Kestler, 2014. "Identifying predictive hubs to condense the training set of $$k$$ -nearest neighbour classifiers," Computational Statistics, Springer, vol. 29(1), pages 81-95, February.
    • Handle: RePEc:spr:compst:v:29:y:2014:i:1:p:81-95
    DOI: 10.1007/s00180-012-0379-0
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    Cited by:

    1. Harald Binder & Hans Kestler & Matthias Schmid, 2014. "Proceedings of Reisensburg 2011," Computational Statistics, Springer, vol. 29(1), pages 1-2, February.
    2. Asma Gul & Aris Perperoglou & Zardad Khan & Osama Mahmoud & Miftahuddin Miftahuddin & Werner Adler & Berthold Lausen, 2018. "Ensemble of a subset of kNN classifiers," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(4), pages 827-840, December.

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