Robust variable selection in the framework of classification with label noise and outliers: applications to spectroscopic data in agri-food

TL;DR

This paper introduces a robust variable selection method for classification tasks involving high-dimensional spectroscopic data, effectively handling label noise and outliers to improve model interpretability and accuracy in agri-food applications.

Contribution

It presents a novel model-based approach that simultaneously performs variable selection, outlier detection, and label noise correction in spectroscopic data classification.

Findings

Effective in identifying anomalous spectra

Reduces problem complexity and enhances robustness

Achieves high predictive accuracy with few wavelengths

Abstract

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.