Estimating expected error rates of neural network classifiers in small sample size situations: a comparison of cross-validation and bootstrap
N. Ueda, Ryoko Nakano · 2002
We compare the cross-validation and bootstrap methods for estimating the expected error rates of feedforward neural network classifiers in small sample size situations. The cross-validation method, a commonly applied method, provides nearly unbiased classification error rates, using only the original samples. The cross-validated estimates, however, may suffer from a large variance. In this paper, we apply a statistical resampling technique, called the bootstrap method, to this estimation problem and compare the performances of these methods. Our results show that the variance of the bootstrap estimates can be smaller than those of the cross-validated estimates.