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.
Lingua originale | English |
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Titolo della pubblicazione ospite | CLADAG 2021 |
Pagine | 49-52 |
Numero di pagine | 4 |
Volume | 128 |
Stato di pubblicazione | Pubblicato - 2021 |
Evento | Scientific Meeting Classification and Data Analysis Group - Firenze Durata: 9 set 2021 → 11 set 2021 |
Convegno
Convegno | Scientific Meeting Classification and Data Analysis Group |
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Città | Firenze |
Periodo | 9/9/21 → 11/9/21 |
Keywords
- Variable selection
- Robust classification
- Label noise
- Agri-food
- Near infrared spectroscopy
- Mid infrared spectroscopy
- Outlier detection