Input and structure selection for k-NN approximator
Antti Sorjamaa, Nima Reyhani and Amaury Lendasse
Computational Intelligence and Bioinspired Systems: 8th International Workshop on Artificial Neural Networks, IWANN 2005, Vilanova i la Geltrú, Barcelona, Spain, June 8-10, 2005. Proceedings
Lecture Notes in Computer Science
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.