Sorting variables by using informative vectors as a strategy for feature selection in multivariate regression

Abstract

A new procedure with high ability to enhance prediction of multivariate calibration models with a small number of interpretable variables is presented. The core of this methodology is to sort the variables from an informative vector, followed by a systematic investigation of PLS regression models with the aim of finding the most relevant set of variables by comparing the cross-validation parameters of the models obtained. In this work, seven main informative vectors i.e. regression vector, correlation vector, residual vector, variable influence on projection (VIP), net analyte signal (NAS), covariance procedures vector (CovProc), signal-to-noise ratios vector (StN) and their combinations were automated and tested with the main purpose of feature selection. Six data sets from different sources were employed to validate this methodology. They originated from: near-Infrared (NIR) spectroscopy, Raman spectroscopy, gas chromatography (GC), fluorescence spectroscopy, quantitative structure-activity relationships (QSAR) and computer simulation. The results indicate that all vectors and their combinations were able to enhance prediction capability with respect to the full data sets. However, regression and NAS informative vectors from partial least squares (PLS) regression, both built using more latent variables than when building the model presented in most of tested data sets, were the best informative vectors for variable selection. In all the applications, the selected variables were quite effective and useful for interpretation.

Estimativa de severidade do mofo-branco em lavouras de feijão utilizando-se sensores hiper e multiespectral

“…5). (Teófilo et al, 2009). Após definido o modelo pela regressão PLS, este foi utilizado nas observações do grupo de dados de validação.…”

“…Os métodos utilizados foram desenvolvidos em Matlab R12.1 (MathWorks, Natick, USA). Para a regressão PLS, utilizou-se o pacote OPS Toolbox para Matlab (Teófilo et al, 2009). O desvio s Qn foi obtido pelo Toolbox Matlab TOMCAT (Daszykowski et al, 2007).…”

White mold detection in common beans through leaf reflectance spectroscopy

“…Hereupon, these values left out from calibration are predicted and prediction residues are computed. The selected wavelengths were the ones that compose a model with the greatest measure quality (TEÓFILO et al 2009). …”

“…Therefore, regression through Partial Least Squares (PLS) is a commonly used multivariate technique that is able to deal with a small number of observations and a great one of variables, in which certain identified "noises" (acquisition imperfections) could be irrelevant and/ or redundant (TEÓFILO et al, 2009). …”

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“…Objective variable ranking techniques such as analysis of variance (ANOVA) (Johnson & Synovec, 2002), the discriminating variable test (DIVA) (Rajalahti et al, 2009a(Rajalahti et al, , 2009b, and informative vectors (Teofilo et al, 2009) have the distinct advantage that variables are ranked based on a mathematically calculable "perceived utility" and not on subjective analyst perception. In essence, the data are given the chance to inform the user of what is relevant and what is likely noise, providing an approach that can be generalized to any set of analytical data.…”