Estimation of error rate for linear discriminant functions by resampling: Non-Gaussian populations

AbstractThis article presents simulation results comparing various resampling estimators of classification error rate for linear discriminant type classification algorithms. Three non-Gaussian multivariate populations are studied namely, exponential, Cauchy and uniform. Simulations are conducted for small sample sizes, two-class and three-class problems and 2-D, 3-D and 5-D distributions. Estimation procedures and sample sizes are the same as in our previous study of Gaussian populations; again 200 bootstrap replications are used for each simulation trial. For exponential and uniform distributions the 0.632 estimator generally performs best. However, for Cauchy distributions the convex bootstrap and the e0 often outperform the 0.632 estimator