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

Andrea Cappozzo, Francesca Greselin () and Thomas Brendan Murphy

Computational Statistics & Data Analysis, 2021, vol. 158, issue C

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.

Keywords: Variable selection; Model-based classification; Label noise; Outliers detection; Wrapper approach; Impartial trimming; Robust estimation (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167947321000207
Full text for ScienceDirect subscribers only.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:158:y:2021:i:c:s0167947321000207

DOI: 10.1016/j.csda.2021.107186

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().