PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Input and structure selection for k-NN approximator
Antti Sorjamaa, Nima Reyhani and Amaury Lendasse
In: 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 , 3512 (XXV). (2005) Springer-Verlag GmbH , Germany , pp. 985-991. ISBN 3-540-26208-3


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.

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Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:1681
Deposited By:Amaury Lendasse
Deposited On:28 November 2005