Evaluating the reliability of spectral variables selected by subsampling methods

Abstract

It is imperfect to evaluate a subsampling variable selection method using only its prediction performance. To further assess the reliability of subsampling variable selection methods, dummy noise variables of different amplitudes were augmented to the original spectral data, and the false variable selection number was recorded. The reliabilities of three subsampling variable selection methods including Monte Carlo uninformative variable elimination (MC‐UVE), competitive adaptive reweighted sampling (CARS), and stability CARS (SCARS) were evaluated using this dummy noise strategy. The evaluation results indicated that both CARS and SCARS produced more parsimonious variable sets, but the reliabilities of their final variable sets were weaker than those of MC‐UVE. On the contrary, only marginal improvement on the prediction performance was obtained using MC‐UVE. Further experiments showed that removing white noise‐like variables beforehand would improve the reliability of variables extracted by CARS and SCARS. Copyright © 2014 John Wiley & Sons, Ltd.