Joint multiple adaptive wavelet regression ensembles

Abstract

Multiple adaptive discrete wavelet transforms were applied during a multiple regression of spectroscopic data for the purpose of investigating the hypothesis — does the use of different wavelets, at different points, within a spectrum, elucidate predictive capability. The model investigated was a constrained stacking regression ensemble with individual regression models chosen initially by a Bayes Metropolis search. The ensemble approach provided the ability to combine different regression models that used different types of wavelets. Models were applied to a publically available dataset, pertaining to biscuit dough, of near infrared spectra, that were measured by a FOSS 5000, and laboratory measurements of the fat, flour, sugar and moisture content.

The resultant model, which is referred to as a joint multiple adaptive wavelet regression ensemble (JMAWRE), was found to be the superior predictive model when compared to models that used standard wavelets as part of the regression ensembles. The JMAWRE was also superior when compared to other models from literature that used the same publicly available NIR dataset.