Input and structure selection for k-NN approximator

Authors:
Antti Sorjamaa;Nima Reyhani;Amaury Lendasse
Affiliations:
Neural Network Research Centre, Helsinki University of Technology, Espoo, Finland;Neural Network Research Centre, Helsinki University of Technology, Espoo, Finland;Neural Network Research Centre, Helsinki University of Technology, Espoo, Finland
Venue:
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Year:
2005

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Abstract

This paper presents k-NN as an approximator for time series prediction problems. The main advantage of this approximator is its simplicity. Despite the simplicity, k-NN can be used to perform input selection for nonlinear models and it also provides accurate approximations. Three model structure selection methods are presented: Leave-one-out, Bootstrap and Bootstrap 632. We will show that both Bootstraps provide a good estimate of the number of neighbors, k, where Leave-one-out fails. Results of the methods are presented with the Electric load from Poland data set.