Robust variable selection for model-based learning in presence of adulteration

TL;DR

This paper introduces two robust variable selection methods for model-based learning that effectively handle outliers and mislabeling, demonstrated through synthetic and real spectroscopic data experiments.

Contribution

It proposes novel robust variable selection techniques that improve feature identification accuracy in contaminated data scenarios.

Findings

Robust methods outperform non-robust ones on synthetic data.

Effective in high-dimensional spectroscopic classification.

Enhanced feature selection in presence of adulteration.

Abstract

The problem of identifying the most discriminating features when performing supervised learning has been extensively investigated. In particular, several methods for variable selection in model-based classification have been proposed. Surprisingly, the impact of outliers and wrongly labeled units on the determination of relevant predictors has received far less attention, with almost no dedicated methodologies available in the literature. In the present paper, we introduce two robust variable selection approaches: one that embeds a robust classifier within a greedy-forward selection procedure and the other based on the theory of maximum likelihood estimation and irrelevance. The former recasts the feature identification as a model selection problem, while the latter regards the relevant subset as a model parameter to be estimated. The benefits of the proposed methods, in contrast with non-robust solutions, are assessed via an experiment on synthetic data. An application to a high-dimensional classification problem of contaminated spectroscopic data concludes the paper.