Evaluating the impact of multivariate imputation by MICE in feature selection.

Handling missing values is a crucial step in preprocessing data in Machine Learning. Most available algorithms for analyzing datasets in the feature selection process and classification or estimation process analyze complete datasets. Consequently, in many cases, the strategy for dealing with missin...

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Autores principales:	Maritza Mera-Gaona, Ursula Neumann, Rubiel Vargas-Canas, Diego M López
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/3daf46d89b7243449bebf01817ceea40
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spelling	oai:doaj.org-article:3daf46d89b7243449bebf01817ceea402021-12-02T20:09:03ZEvaluating the impact of multivariate imputation by MICE in feature selection.1932-620310.1371/journal.pone.0254720https://doaj.org/article/3daf46d89b7243449bebf01817ceea402021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0254720https://doaj.org/toc/1932-6203Handling missing values is a crucial step in preprocessing data in Machine Learning. Most available algorithms for analyzing datasets in the feature selection process and classification or estimation process analyze complete datasets. Consequently, in many cases, the strategy for dealing with missing values is to use only instances with full data or to replace missing values with a mean, mode, median, or a constant value. Usually, discarding missing samples or replacing missing values by means of fundamental techniques causes bias in subsequent analyzes on datasets.<h4>Aim</h4>Demonstrate the positive impact of multivariate imputation in the feature selection process on datasets with missing values.<h4>Results</h4>We compared the effects of the feature selection process using complete datasets, incomplete datasets with missingness rates between 5 and 50%, and imputed datasets by basic techniques and multivariate imputation. The feature selection algorithms used are well-known methods. The results showed that the datasets imputed by multivariate imputation obtained the best results in feature selection compared to datasets imputed by basic techniques or non-imputed incomplete datasets.<h4>Conclusions</h4>Considering the results obtained in the evaluation, applying multivariate imputation by MICE reduces bias in the feature selection process.Maritza Mera-GaonaUrsula NeumannRubiel Vargas-CanasDiego M LópezPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0254720 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Maritza Mera-Gaona Ursula Neumann Rubiel Vargas-Canas Diego M López Evaluating the impact of multivariate imputation by MICE in feature selection.
description	Handling missing values is a crucial step in preprocessing data in Machine Learning. Most available algorithms for analyzing datasets in the feature selection process and classification or estimation process analyze complete datasets. Consequently, in many cases, the strategy for dealing with missing values is to use only instances with full data or to replace missing values with a mean, mode, median, or a constant value. Usually, discarding missing samples or replacing missing values by means of fundamental techniques causes bias in subsequent analyzes on datasets.<h4>Aim</h4>Demonstrate the positive impact of multivariate imputation in the feature selection process on datasets with missing values.<h4>Results</h4>We compared the effects of the feature selection process using complete datasets, incomplete datasets with missingness rates between 5 and 50%, and imputed datasets by basic techniques and multivariate imputation. The feature selection algorithms used are well-known methods. The results showed that the datasets imputed by multivariate imputation obtained the best results in feature selection compared to datasets imputed by basic techniques or non-imputed incomplete datasets.<h4>Conclusions</h4>Considering the results obtained in the evaluation, applying multivariate imputation by MICE reduces bias in the feature selection process.
format	article
author	Maritza Mera-Gaona Ursula Neumann Rubiel Vargas-Canas Diego M López
author_facet	Maritza Mera-Gaona Ursula Neumann Rubiel Vargas-Canas Diego M López
author_sort	Maritza Mera-Gaona
title	Evaluating the impact of multivariate imputation by MICE in feature selection.
title_short	Evaluating the impact of multivariate imputation by MICE in feature selection.
title_full	Evaluating the impact of multivariate imputation by MICE in feature selection.
title_fullStr	Evaluating the impact of multivariate imputation by MICE in feature selection.
title_full_unstemmed	Evaluating the impact of multivariate imputation by MICE in feature selection.
title_sort	evaluating the impact of multivariate imputation by mice in feature selection.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/3daf46d89b7243449bebf01817ceea40
work_keys_str_mv	AT maritzameragaona evaluatingtheimpactofmultivariateimputationbymiceinfeatureselection AT ursulaneumann evaluatingtheimpactofmultivariateimputationbymiceinfeatureselection AT rubielvargascanas evaluatingtheimpactofmultivariateimputationbymiceinfeatureselection AT diegomlopez evaluatingtheimpactofmultivariateimputationbymiceinfeatureselection
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Evaluating the impact of multivariate imputation by MICE in feature selection.

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