Abstract
AbstractThrough the remarkable progress in technology, it is getting easier and easier to generate vast amounts of variables from a given sample. The selection of variables is imperative for data reduction and for understanding the modeled relationship. Partial least squares (PLS) regression is among the modeling approaches that address high throughput data. A considerable list of variable selection methods has been introduced in PLS. Most of these methods have been reviewed in a recently conducted study. Motivated by this, we have therefore conducted a comparison of available methods for variable selection within PLS. The main focus of this study was to reveal patterns of dependencies between variable selection method and data properties, which can guide the choice of method in practical data analysis. To this aim, a simulation study was conducted with data sets having diverse properties like the number of variables, the number of samples, model complexity level, and information content. The results indicate that the above factors like the number of variables, number of samples, model complexity level, information content and variant of PLS methods, and their mutual higher‐order interactions all significantly define the prediction capabilities of the model and the choice of variable selection strategy.
Highlights
IntroductionThanks to the massive use of data generation technologies (spectroscopy, RNAs, satellite images, brain images, etc), a huge amount of data is created in many real-life applications
Thanks to the massive use of data generation technologies, a huge amount of data is created in many real-life applications
This study provides the comparison of 17 variable selection methods in Partial least squares (PLS)
Summary
Thanks to the massive use of data generation technologies (spectroscopy, RNAs, satellite images, brain images, etc), a huge amount of data is created in many real-life applications. It enables economic, speedy, and efficient generation of information (variables) of given objects (samples). In order to understand the complexity behind such high-dimensional data sets, multivariate approaches are mandatory to consider. The negative aspect of data generation technologies is the inclusion of irrelevant variables. These irrelevant variables result in a declination of the model performance, in amplification of model complexity, and in the reduction of the understandability of modeled relations. Exclusion of irrelevant variables is important.[1,2,3,4]
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