RENT—Repeated Elastic Net Technique for Feature Selection

Feature selection is an essential step in data science pipelines to reduce the complexity associated with large datasets. While much research on this topic focuses on optimizing predictive performance, few studies investigate stability in the context of the feature selection process. In this study,...

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Autores principales:	Anna Jenul, Stefan Schrunner, Kristian Hovde Liland, Ulf Geir Indahl, Cecilia Marie Futsaether, Oliver Tomic
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Elastic net regularization exploratory analysis ensemble feature selection generalized linear models selection stability Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/5b1acb3b57a54811ba6a16ee486dffd9
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spelling	oai:doaj.org-article:5b1acb3b57a54811ba6a16ee486dffd92021-11-20T00:02:45ZRENT—Repeated Elastic Net Technique for Feature Selection2169-353610.1109/ACCESS.2021.3126429https://doaj.org/article/5b1acb3b57a54811ba6a16ee486dffd92021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9606766/https://doaj.org/toc/2169-3536Feature selection is an essential step in data science pipelines to reduce the complexity associated with large datasets. While much research on this topic focuses on optimizing predictive performance, few studies investigate stability in the context of the feature selection process. In this study, we present the Repeated Elastic Net Technique (RENT) for Feature Selection. RENT uses an ensemble of generalized linear models with elastic net regularization, each trained on distinct subsets of the training data. The feature selection is based on three criteria evaluating the weight distributions of features across all elementary models. This fact leads to the selection of features with high stability that improve the robustness of the final model. Furthermore, unlike established feature selectors, RENT provides valuable information for model interpretation concerning the identification of objects in the data that are difficult to predict during training. In our experiments, we benchmark RENT against six established feature selectors on eight multivariate datasets for binary classification and regression. In the experimental comparison, RENT shows a well-balanced trade-off between predictive performance and stability. Finally, we underline the additional interpretational value of RENT with an exploratory post-hoc analysis of a healthcare dataset.Anna JenulStefan SchrunnerKristian Hovde LilandUlf Geir IndahlCecilia Marie FutsaetherOliver TomicIEEEarticleElastic net regularizationexploratory analysisensemble feature selectiongeneralized linear modelsselection stabilityElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 152333-152346 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Elastic net regularization exploratory analysis ensemble feature selection generalized linear models selection stability Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Elastic net regularization exploratory analysis ensemble feature selection generalized linear models selection stability Electrical engineering. Electronics. Nuclear engineering TK1-9971 Anna Jenul Stefan Schrunner Kristian Hovde Liland Ulf Geir Indahl Cecilia Marie Futsaether Oliver Tomic RENT—Repeated Elastic Net Technique for Feature Selection
description	Feature selection is an essential step in data science pipelines to reduce the complexity associated with large datasets. While much research on this topic focuses on optimizing predictive performance, few studies investigate stability in the context of the feature selection process. In this study, we present the Repeated Elastic Net Technique (RENT) for Feature Selection. RENT uses an ensemble of generalized linear models with elastic net regularization, each trained on distinct subsets of the training data. The feature selection is based on three criteria evaluating the weight distributions of features across all elementary models. This fact leads to the selection of features with high stability that improve the robustness of the final model. Furthermore, unlike established feature selectors, RENT provides valuable information for model interpretation concerning the identification of objects in the data that are difficult to predict during training. In our experiments, we benchmark RENT against six established feature selectors on eight multivariate datasets for binary classification and regression. In the experimental comparison, RENT shows a well-balanced trade-off between predictive performance and stability. Finally, we underline the additional interpretational value of RENT with an exploratory post-hoc analysis of a healthcare dataset.
format	article
author	Anna Jenul Stefan Schrunner Kristian Hovde Liland Ulf Geir Indahl Cecilia Marie Futsaether Oliver Tomic
author_facet	Anna Jenul Stefan Schrunner Kristian Hovde Liland Ulf Geir Indahl Cecilia Marie Futsaether Oliver Tomic
author_sort	Anna Jenul
title	RENT—Repeated Elastic Net Technique for Feature Selection
title_short	RENT—Repeated Elastic Net Technique for Feature Selection
title_full	RENT—Repeated Elastic Net Technique for Feature Selection
title_fullStr	RENT—Repeated Elastic Net Technique for Feature Selection
title_full_unstemmed	RENT—Repeated Elastic Net Technique for Feature Selection
title_sort	rent—repeated elastic net technique for feature selection
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/5b1acb3b57a54811ba6a16ee486dffd9
work_keys_str_mv	AT annajenul rentx2014repeatedelasticnettechniqueforfeatureselection AT stefanschrunner rentx2014repeatedelasticnettechniqueforfeatureselection AT kristianhovdeliland rentx2014repeatedelasticnettechniqueforfeatureselection AT ulfgeirindahl rentx2014repeatedelasticnettechniqueforfeatureselection AT ceciliamariefutsaether rentx2014repeatedelasticnettechniqueforfeatureselection AT olivertomic rentx2014repeatedelasticnettechniqueforfeatureselection
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