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HDSI: High dimensional selection with interactions algorithm on feature selection and testing

Feature selection on high dimensional data along with the interaction effects is a critical challenge for classical statistical learning techniques. Existing feature selection algorithms such as random LASSO leverages LASSO capability to handle high dimensional data. However, the technique has two m...

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Detalles Bibliográficos
Autores principales: Jain, Rahi, Xu, Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7886179/
https://www.ncbi.nlm.nih.gov/pubmed/33592034
http://dx.doi.org/10.1371/journal.pone.0246159
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author Jain, Rahi
Xu, Wei
author_facet Jain, Rahi
Xu, Wei
author_sort Jain, Rahi
collection PubMed
description Feature selection on high dimensional data along with the interaction effects is a critical challenge for classical statistical learning techniques. Existing feature selection algorithms such as random LASSO leverages LASSO capability to handle high dimensional data. However, the technique has two main limitations, namely the inability to consider interaction terms and the lack of a statistical test for determining the significance of selected features. This study proposes a High Dimensional Selection with Interactions (HDSI) algorithm, a new feature selection method, which can handle high-dimensional data, incorporate interaction terms, provide the statistical inferences of selected features and leverage the capability of existing classical statistical techniques. The method allows the application of any statistical technique like LASSO and subset selection on multiple bootstrapped samples; each contains randomly selected features. Each bootstrap data incorporates interaction terms for the randomly sampled features. The selected features from each model are pooled and their statistical significance is determined. The selected statistically significant features are used as the final output of the approach, whose final coefficients are estimated using appropriate statistical techniques. The performance of HDSI is evaluated using both simulated data and real studies. In general, HDSI outperforms the commonly used algorithms such as LASSO, subset selection, adaptive LASSO, random LASSO and group LASSO.
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spelling pubmed-78861792021-02-23 HDSI: High dimensional selection with interactions algorithm on feature selection and testing Jain, Rahi Xu, Wei PLoS One Research Article Feature selection on high dimensional data along with the interaction effects is a critical challenge for classical statistical learning techniques. Existing feature selection algorithms such as random LASSO leverages LASSO capability to handle high dimensional data. However, the technique has two main limitations, namely the inability to consider interaction terms and the lack of a statistical test for determining the significance of selected features. This study proposes a High Dimensional Selection with Interactions (HDSI) algorithm, a new feature selection method, which can handle high-dimensional data, incorporate interaction terms, provide the statistical inferences of selected features and leverage the capability of existing classical statistical techniques. The method allows the application of any statistical technique like LASSO and subset selection on multiple bootstrapped samples; each contains randomly selected features. Each bootstrap data incorporates interaction terms for the randomly sampled features. The selected features from each model are pooled and their statistical significance is determined. The selected statistically significant features are used as the final output of the approach, whose final coefficients are estimated using appropriate statistical techniques. The performance of HDSI is evaluated using both simulated data and real studies. In general, HDSI outperforms the commonly used algorithms such as LASSO, subset selection, adaptive LASSO, random LASSO and group LASSO. Public Library of Science 2021-02-16 /pmc/articles/PMC7886179/ /pubmed/33592034 http://dx.doi.org/10.1371/journal.pone.0246159 Text en © 2021 Jain, Xu http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Jain, Rahi
Xu, Wei
HDSI: High dimensional selection with interactions algorithm on feature selection and testing
title HDSI: High dimensional selection with interactions algorithm on feature selection and testing
title_full HDSI: High dimensional selection with interactions algorithm on feature selection and testing
title_fullStr HDSI: High dimensional selection with interactions algorithm on feature selection and testing
title_full_unstemmed HDSI: High dimensional selection with interactions algorithm on feature selection and testing
title_short HDSI: High dimensional selection with interactions algorithm on feature selection and testing
title_sort hdsi: high dimensional selection with interactions algorithm on feature selection and testing
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7886179/
https://www.ncbi.nlm.nih.gov/pubmed/33592034
http://dx.doi.org/10.1371/journal.pone.0246159
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