Mathematical programming for simultaneous feature selection and outlier detection under l1 norm

Michele Barbato and Alberto Ceselli

European Journal of Operational Research, 2024, vol. 316, issue 3, 1070-1084

Abstract: The goal of simultaneous feature selection and outlier detection is to determine a sparse linear regression vector by fitting a dataset possibly affected by the presence of outliers.

Keywords: Data science; Outlier detection; Feature selection; Least absolute deviation; Mathematical programming (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:316:y:2024:i:3:p:1070-1084

DOI: 10.1016/j.ejor.2024.03.035

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