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.

Cappozzo, A., Duponchel, L., Greselin, F., Murphy, B., Robust classification of spectroscopic data in agri-food: First analysis on the stability of results, Comunicazione, in CLADAG 2021, (Firenze, 09-11 September 2021), Firenze University Press, Firenze 2021:128 49-52 [https://hdl.handle.net/10807/306439]

Robust classification of spectroscopic data in agri-food: First analysis on the stability of results

Cappozzo, Andrea;
2021

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.
2021
Inglese
CLADAG 2021
Scientific Meeting Classification and Data Analysis Group
Firenze
Comunicazione
9-set-2021
11-set-2021
978-88-5518-340-6
Firenze University Press
Cappozzo, A., Duponchel, L., Greselin, F., Murphy, B., Robust classification of spectroscopic data in agri-food: First analysis on the stability of results, Comunicazione, in CLADAG 2021, (Firenze, 09-11 September 2021), Firenze University Press, Firenze 2021:128 49-52 [https://hdl.handle.net/10807/306439]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10807/306439
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