Input and Structure Selection for k-NN Approximator

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.

• Type: Conference
• Year: 2005
• Where: IWANN
• Authors: Antti Sorjamaa, Nima Reyhani, Amaury Lendasse
• Keywords: IWANN 2005, Leave-one-out Fails, Series Prediction Problems, Structure Selection Methods