TY - JOUR
T1 - Robust variable selection in the framework of classification with label noise and outliers: Applications to spectroscopic data in agri-food
AU - Cappozzo, Andrea
AU - Duponchel, L.
AU - Greselin, F.
AU - Murphy, T. B.
PY - 2021
Y1 - 2021
N2 - Classification of high-dimensional spectroscopic data is a common task in analytical chemistry. Well-established procedures like support vector machines (SVMs) and partial least squares discriminant analysis (PLS-DA) are the most common methods for tackling this supervised learning problem. Nonetheless, interpretation of these models remains sometimes difficult, and solutions based on feature selection are often adopted as they lead to the automatic identification of the most informative wavelengths. Unfortunately, for some delicate applications like food authenticity, mislabeled and adulterated spectra occur both in the calibration and/or validation sets, with dramatic effects on the model development, its prediction accuracy and robustness. Motivated by these issues, the present paper proposes a robust model-based method that simultaneously performs variable selection, outliers and label noise detection. We demonstrate the effectiveness of our proposal in dealing with three agri-food spectroscopic studies, where several forms of perturbations are considered. Our approach succeeds in diminishing problem complexity, identifying anomalous spectra and attaining competitive predictive accuracy considering a very low number of selected wavelengths.
AB - Classification of high-dimensional spectroscopic data is a common task in analytical chemistry. Well-established procedures like support vector machines (SVMs) and partial least squares discriminant analysis (PLS-DA) are the most common methods for tackling this supervised learning problem. Nonetheless, interpretation of these models remains sometimes difficult, and solutions based on feature selection are often adopted as they lead to the automatic identification of the most informative wavelengths. Unfortunately, for some delicate applications like food authenticity, mislabeled and adulterated spectra occur both in the calibration and/or validation sets, with dramatic effects on the model development, its prediction accuracy and robustness. Motivated by these issues, the present paper proposes a robust model-based method that simultaneously performs variable selection, outliers and label noise detection. We demonstrate the effectiveness of our proposal in dealing with three agri-food spectroscopic studies, where several forms of perturbations are considered. Our approach succeeds in diminishing problem complexity, identifying anomalous spectra and attaining competitive predictive accuracy considering a very low number of selected wavelengths.
KW - Agri-food
KW - Label noise
KW - Mid infrared spectroscopy
KW - Variable selection
KW - Outlier detection
KW - Robust classification
KW - Near infrared spectroscopy
KW - Agri-food
KW - Label noise
KW - Mid infrared spectroscopy
KW - Variable selection
KW - Outlier detection
KW - Robust classification
KW - Near infrared spectroscopy
UR - http://hdl.handle.net/10807/306445
U2 - 10.1016/j.aca.2021.338245
DO - 10.1016/j.aca.2021.338245
M3 - Article
SN - 0003-2670
VL - 1153
SP - 1
EP - 14
JO - Analytica Chimica Acta
JF - Analytica Chimica Acta
ER -