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

Content Provider	Springer Nature Link
Author	Lausser, Ludwig Müssel, Christoph Melkozerov, Alexander Kestler, Hans A.
Copyright Year	2012
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.
Starting Page	81
Ending Page	95
Page Count	15
File Format	PDF
ISSN	09434062
Journal	Computational Statistics
Volume Number	29
Issue Number	1-2
e-ISSN	16139658
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2012-11-22
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	$$k$$ -Nearest neighbour Classification Genetic algorithm Predictive hubs Statistics Probability and Statistics in Computer Science Probability Theory and Stochastic Processes Economic Theory
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Statistics, Probability and Uncertainty Computational Mathematics

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