The problem of identifying the most discriminating features when performing supervised learning has been extensively investigated. In particular, several methods for variable selection have been proposed in model-based classification. The impact of outliers and wrongly labeled units on the determination of relevant predictors has instead received far less attention, with almost no dedicated methodologies available. Two robust variable selection approaches are introduced: 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 is presented.

Cappozzo, A., Greselin, F., & Murphy, T. (2021). Robust variable selection for model-based learning in presence of adulteration. COMPUTATIONAL STATISTICS & DATA ANALYSIS, 158 [10.1016/j.csda.2021.107186].

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

Cappozzo, Andrea
;
Greselin, Francesca
;
2021

Abstract

The problem of identifying the most discriminating features when performing supervised learning has been extensively investigated. In particular, several methods for variable selection have been proposed in model-based classification. The impact of outliers and wrongly labeled units on the determination of relevant predictors has instead received far less attention, with almost no dedicated methodologies available. Two robust variable selection approaches are introduced: 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 is presented.
Articolo in rivista - Articolo scientifico
Impartial trimming; Label noise; Model-based classification; Outliers detection; Robust estimation; Variable selection; Wrapper approach;
English
21
Cappozzo, A., Greselin, F., & Murphy, T. (2021). Robust variable selection for model-based learning in presence of adulteration. COMPUTATIONAL STATISTICS & DATA ANALYSIS, 158 [10.1016/j.csda.2021.107186].
Cappozzo, A; Greselin, F; Murphy, T
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/305670
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