Input and Structure Selection for k-NN Approximator | Zendy

Antti Sorjamaa | Zendy; Nima Reyhani | Zendy; Amaury Lendasse | Zendy

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Input and Structure Selection for k-NN Approximator

Author(s) -

Antti Sorjamaa,

Nima Reyhani,

Amaury Lendasse

Publication year - 2005

Publication title -

lecture notes in computer science

Language(s) - English

Resource type - Book series

SCImago Journal Rank - 0.249

H-Index - 400

eISSN - 1611-3349

pISSN - 0302-9743

ISBN - 3-540-26208-3

DOI - 10.1007/11494669_121

Subject(s) - computer science , simplicity , selection (genetic algorithm) , set (abstract data type) , nonlinear system , series (stratigraphy) , algorithm , model selection , time series , mathematical optimization , artificial intelligence , machine learning , data mining , mathematics , paleontology , philosophy , physics , epistemology , quantum mechanics , biology , programming language

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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