We investigate here the stability of the obtained results of a variable selection method recently introduced in the literature, and embedded into a modelbased classification framework. It is applied to chemometric data, with the purpose of selecting a few wavenumbers (of the order of tens) among the thousands measured ones, to build a (robust) decision rule for classification. The robust nature of the method safeguards it from potential label noise and outliers, which are particularly dangerous in the field of food-authenticity studies. As a by-product of the learning process, samples are grouped into similar classes, and anomalous samples are also singled out. Our first results show that there is some variability around a common pattern in the obtained selection.
Robust classification of spectroscopic data in agri-food: First analysis on the stability of results
Andrea Cappozzo;
2021-01-01
Abstract
We investigate here the stability of the obtained results of a variable selection method recently introduced in the literature, and embedded into a modelbased classification framework. It is applied to chemometric data, with the purpose of selecting a few wavenumbers (of the order of tens) among the thousands measured ones, to build a (robust) decision rule for classification. The robust nature of the method safeguards it from potential label noise and outliers, which are particularly dangerous in the field of food-authenticity studies. As a by-product of the learning process, samples are grouped into similar classes, and anomalous samples are also singled out. Our first results show that there is some variability around a common pattern in the obtained selection.File | Dimensione | Formato | |
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CDGM Cladag2021 Stability results on robust variable selection for classification of spectroscopic data.pdf
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