Bootstrap methods for assessing the performance of near-infrared pattern classification techniques

Two parametric bootstrap techniques were applied to near-infrared (NIR) pattern classification models for two classes of microcrystalline cellulose, Avicel(R) PH101 and PH102, which differ only in particle size. The development of pattern classification models for similar substances is difficult, since their characteristic clusters overlap. Bootstrapping was used to enlarge small test sets for a better approximation of the overlapping area of these nearly identical substances, consequently resulting in better estimates of misclassification rates. A bootstrap that resampled the residuals, referred to as the outside model space bootstrap in this paper, and a novel bootstrap that resampled principal component scores, referred to as the inside model space bootstrap, were studied. A comparison revealed that classification rates for both bootstrap techniques were similar to the original test set classification rates. The bootstrap method developed in this study, which resampled the principal component scores, was more effective for estimating misclassification volumes than the residual-resampling method. Copyright (C) 2002 John Wiley Sons, Ltd.