Nonparametric Radial Basis Function Regression Using

Genetic Algorithms and Model Averaging


Andrea Nibbe, Statistics Department, Colorado State University

Thursday, 11 December 2003

3:10 PM

E106 Engineering Building


This work proposes a model selection approach to radial basis functions where the number and locations of basis functions is treated as an unknown.  The genetic algorithm is used to optimize the basis function placements, where the measure of a model's performance is defined by Akaike's  Information criterion (AIC).  Predictions are made by applying model averaging strategies to the top performing models sampled throughout the genetic algorithm search.


The most promising of the model averaging strategies involves fitting a weighted least squares regression to the predicted values from the top models.  The input weights are the inverse of the AIC value from the corresponding model fit. Simulation results show that the proposed method outperforms a Bayesian approach to the same problem.










College of Natural Sciences




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