Variance reduction in estimating classification error using sparse datasets

Abstract

In biomedical applications, frequently only a limited number of samples are available for the development and testing of classification rules. Understanding the behavior of the error estimators in this setting is therefore highly desirable. In an extensive study using simulated as well as real-life data we investigated the properties of commonly used error estimators in terms of their bias and variance, and have found that in these small-sample size situations, the influence of variance on the error estimates can be significant, and can dominate the bias. Consequently, our results strongly suggest that bootstrap resampling and/or k-fold crossvalidation-based estimators, especially when computed over multiple data splits, should be preferred in these small-sample size scenarios, because of their reduced variance compared to the more routinely used crossvalidation approaches. While linear partial least squares was used as the classifier/regressor, the general conclusions arising from this study are not qualitatively affected for other classifiers, linear or nonlinear.