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EFS: an ensemble feature selection tool implemented as R-package and web-application

BACKGROUND: Feature selection methods aim at identifying a subset of features that improve the prediction performance of subsequent classification models and thereby also simplify their interpretability. Preceding studies demonstrated that single feature selection methods can have specific biases, w...

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Detalles Bibliográficos
Autores principales: Neumann, Ursula, Genze, Nikita, Heider, Dominik
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5488355/
https://www.ncbi.nlm.nih.gov/pubmed/28674556
http://dx.doi.org/10.1186/s13040-017-0142-8
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author Neumann, Ursula
Genze, Nikita
Heider, Dominik
author_facet Neumann, Ursula
Genze, Nikita
Heider, Dominik
author_sort Neumann, Ursula
collection PubMed
description BACKGROUND: Feature selection methods aim at identifying a subset of features that improve the prediction performance of subsequent classification models and thereby also simplify their interpretability. Preceding studies demonstrated that single feature selection methods can have specific biases, whereas an ensemble feature selection has the advantage to alleviate and compensate for these biases. RESULTS: The software EFS (Ensemble Feature Selection) makes use of multiple feature selection methods and combines their normalized outputs to a quantitative ensemble importance. Currently, eight different feature selection methods have been integrated in EFS, which can be used separately or combined in an ensemble. CONCLUSION: EFS identifies relevant features while compensating specific biases of single methods due to an ensemble approach. Thereby, EFS can improve the prediction accuracy and interpretability in subsequent binary classification models. AVAILABILITY: EFS can be downloaded as an R-package from CRAN or used via a web application at http://EFS.heiderlab.de.
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spelling pubmed-54883552017-07-03 EFS: an ensemble feature selection tool implemented as R-package and web-application Neumann, Ursula Genze, Nikita Heider, Dominik BioData Min Software Article BACKGROUND: Feature selection methods aim at identifying a subset of features that improve the prediction performance of subsequent classification models and thereby also simplify their interpretability. Preceding studies demonstrated that single feature selection methods can have specific biases, whereas an ensemble feature selection has the advantage to alleviate and compensate for these biases. RESULTS: The software EFS (Ensemble Feature Selection) makes use of multiple feature selection methods and combines their normalized outputs to a quantitative ensemble importance. Currently, eight different feature selection methods have been integrated in EFS, which can be used separately or combined in an ensemble. CONCLUSION: EFS identifies relevant features while compensating specific biases of single methods due to an ensemble approach. Thereby, EFS can improve the prediction accuracy and interpretability in subsequent binary classification models. AVAILABILITY: EFS can be downloaded as an R-package from CRAN or used via a web application at http://EFS.heiderlab.de. BioMed Central 2017-06-27 /pmc/articles/PMC5488355/ /pubmed/28674556 http://dx.doi.org/10.1186/s13040-017-0142-8 Text en © The Author(s) 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Software Article
Neumann, Ursula
Genze, Nikita
Heider, Dominik
EFS: an ensemble feature selection tool implemented as R-package and web-application
title EFS: an ensemble feature selection tool implemented as R-package and web-application
title_full EFS: an ensemble feature selection tool implemented as R-package and web-application
title_fullStr EFS: an ensemble feature selection tool implemented as R-package and web-application
title_full_unstemmed EFS: an ensemble feature selection tool implemented as R-package and web-application
title_short EFS: an ensemble feature selection tool implemented as R-package and web-application
title_sort efs: an ensemble feature selection tool implemented as r-package and web-application
topic Software Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5488355/
https://www.ncbi.nlm.nih.gov/pubmed/28674556
http://dx.doi.org/10.1186/s13040-017-0142-8
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