Self-Organizing Maps for Imputation of Missing Data in Incomplete Data Matrices

Autores
Folguera, Laura; Zupan, Jure; Cicerone, Daniel; Magallanes, Jorge
Año de publicación
2015
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The problem of incomplete data matrices is repeatedly found in large databases; posing a significant obstacle for an effective treatment of data. This paper examines a self-organizing-map (SOM) based method of data imputation under the concept of distance object per one weight; to predict physicochemical parameters of water samples in a data set where concentrations of different analytes were missed. The method was evaluated according to two different possibilities: (a) including vectors of samples with and without missing data in the training data set and (b) pre-training a SOM for a data set with no missing values and then making imputations for a second data set (prediction set) of samples with missing values. Evaluations were made using a surface water data set of 270 samples from Reconquista River; in Buenos Aires Province; Argentina; by artificially setting a range of 17% to 39% of the data to missing. Results were compared to imputations made through professional criteria. SOMs gave reasonable estimates; with no statistically significant differences from estimates made through professional criteria; proving thus to be a suitable time-saving imputation method.
Fil: Laura Folguera. Universidad Nacional de San Martín. Instituto de Investigación e Ingeniería Ambiental; Buenos Aires. Argentina.
Fil: Jure Zupan. National Institute of Chemistry; Ljubljana. Slovenia.
Fil: Daniel Cicerone. Universidad Nacional de San Martín. Instituto de Investigación e Ingeniería Ambiental; Buenos Aires. Argentina.
Fil: Jorge Magallanes. Universidad Nacional de San Martín. Instituto de Investigación e Ingeniería Ambiental; Buenos Aires. Argentina.
Fuente
Chemometrics and Intelligent Laboratory Systems. 143: 146-151 (2015) Elsevier B.V.
http://dx.doi.org/10.1016/j.chemolab.2015.03.002
Materia
CHEMOMETRICS
ARTIFICIAL NEURAL NETWORK
SELF-ORGANIZING MAPS
MISSING DATA IMPUTATION
ENVIRONMENTAL DATA SET
CIENCIAS QUÍMICAS
CIENCIAS EXACTAS Y NATURALES
Nivel de accesibilidad
acceso restringido
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
Repositorio Institucional (UNSAM)
Institución
Universidad Nacional de General San Martín
OAI Identificador
oai:ri.unsam.edu.ar:123456789/1009
Acceder
id RIUNSAM_38bbb9fe520b192a07130ae0912e128a
oai_identifier_str oai:ri.unsam.edu.ar:123456789/1009
network_acronym_str RIUNSAM
repository_id_str s
network_name_str Repositorio Institucional (UNSAM)
spelling Self-Organizing Maps for Imputation of Missing Data in Incomplete Data MatricesFolguera, LauraZupan, JureCicerone, DanielMagallanes, JorgeCHEMOMETRICSARTIFICIAL NEURAL NETWORKSELF-ORGANIZING MAPSMISSING DATA IMPUTATIONENVIRONMENTAL DATA SETCIENCIAS QUÍMICASCIENCIAS EXACTAS Y NATURALESThe problem of incomplete data matrices is repeatedly found in large databases; posing a significant obstacle for an effective treatment of data. This paper examines a self-organizing-map (SOM) based method of data imputation under the concept of distance object per one weight; to predict physicochemical parameters of water samples in a data set where concentrations of different analytes were missed. The method was evaluated according to two different possibilities: (a) including vectors of samples with and without missing data in the training data set and (b) pre-training a SOM for a data set with no missing values and then making imputations for a second data set (prediction set) of samples with missing values. Evaluations were made using a surface water data set of 270 samples from Reconquista River; in Buenos Aires Province; Argentina; by artificially setting a range of 17% to 39% of the data to missing. Results were compared to imputations made through professional criteria. SOMs gave reasonable estimates; with no statistically significant differences from estimates made through professional criteria; proving thus to be a suitable time-saving imputation method.Fil: Laura Folguera. Universidad Nacional de San Martín. Instituto de Investigación e Ingeniería Ambiental; Buenos Aires. Argentina.Fil: Jure Zupan. National Institute of Chemistry; Ljubljana. Slovenia.Fil: Daniel Cicerone. Universidad Nacional de San Martín. Instituto de Investigación e Ingeniería Ambiental; Buenos Aires. Argentina.Fil: Jorge Magallanes. Universidad Nacional de San Martín. Instituto de Investigación e Ingeniería Ambiental; Buenos Aires. Argentina.Elsevier Science Bv2015-03info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfpp. 146-151application/pdfFolguera, L. et al (2015). Self-Organizing Maps for Imputation of Missing Data in Incomplete Data Matrices. En: Chemometrics and Intelligent Laboratory Systems. Elsevier Science 143, 146-1510169-7439https://ri.unsam.edu.ar/handle/123456789/1009Chemometrics and Intelligent Laboratory Systems. 143: 146-151 (2015) Elsevier B.V.http://dx.doi.org/10.1016/j.chemolab.2015.03.002reponame:Repositorio Institucional (UNSAM)instname:Universidad Nacional de General San Martínenginfo:eu-repo/semantics/restrictedAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Atribución-NoComercial-CompartirIgual 2.5 Argentina (CC BY-NC-SA 2.5)2025-09-29T14:30:21Zoai:ri.unsam.edu.ar:123456789/1009instacron:UNSAMInstitucionalhttp://ri.unsam.edu.arUniversidad públicaNo correspondehttp://ri.unsam.edu.ar/oai/lpastran@unsam.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:s2025-09-29 14:31:15.548Repositorio Institucional (UNSAM) - Universidad Nacional de General San Martínfalse
dc.title.none.fl_str_mv Self-Organizing Maps for Imputation of Missing Data in Incomplete Data Matrices
title Self-Organizing Maps for Imputation of Missing Data in Incomplete Data Matrices
spellingShingle Self-Organizing Maps for Imputation of Missing Data in Incomplete Data Matrices
Folguera, Laura
CHEMOMETRICS
ARTIFICIAL NEURAL NETWORK
SELF-ORGANIZING MAPS
MISSING DATA IMPUTATION
ENVIRONMENTAL DATA SET
CIENCIAS QUÍMICAS
CIENCIAS EXACTAS Y NATURALES
title_short Self-Organizing Maps for Imputation of Missing Data in Incomplete Data Matrices
title_full Self-Organizing Maps for Imputation of Missing Data in Incomplete Data Matrices
title_fullStr Self-Organizing Maps for Imputation of Missing Data in Incomplete Data Matrices
title_full_unstemmed Self-Organizing Maps for Imputation of Missing Data in Incomplete Data Matrices
title_sort Self-Organizing Maps for Imputation of Missing Data in Incomplete Data Matrices
dc.creator.none.fl_str_mv Folguera, Laura
Zupan, Jure
Cicerone, Daniel
Magallanes, Jorge
author Folguera, Laura
author_facet Folguera, Laura
Zupan, Jure
Cicerone, Daniel
Magallanes, Jorge
author_role author
author2 Zupan, Jure
Cicerone, Daniel
Magallanes, Jorge
author2_role author
author
author
dc.subject.none.fl_str_mv CHEMOMETRICS
ARTIFICIAL NEURAL NETWORK
SELF-ORGANIZING MAPS
MISSING DATA IMPUTATION
ENVIRONMENTAL DATA SET
CIENCIAS QUÍMICAS
CIENCIAS EXACTAS Y NATURALES
topic CHEMOMETRICS
ARTIFICIAL NEURAL NETWORK
SELF-ORGANIZING MAPS
MISSING DATA IMPUTATION
ENVIRONMENTAL DATA SET
CIENCIAS QUÍMICAS
CIENCIAS EXACTAS Y NATURALES
dc.description.none.fl_txt_mv The problem of incomplete data matrices is repeatedly found in large databases; posing a significant obstacle for an effective treatment of data. This paper examines a self-organizing-map (SOM) based method of data imputation under the concept of distance object per one weight; to predict physicochemical parameters of water samples in a data set where concentrations of different analytes were missed. The method was evaluated according to two different possibilities: (a) including vectors of samples with and without missing data in the training data set and (b) pre-training a SOM for a data set with no missing values and then making imputations for a second data set (prediction set) of samples with missing values. Evaluations were made using a surface water data set of 270 samples from Reconquista River; in Buenos Aires Province; Argentina; by artificially setting a range of 17% to 39% of the data to missing. Results were compared to imputations made through professional criteria. SOMs gave reasonable estimates; with no statistically significant differences from estimates made through professional criteria; proving thus to be a suitable time-saving imputation method.
Fil: Laura Folguera. Universidad Nacional de San Martín. Instituto de Investigación e Ingeniería Ambiental; Buenos Aires. Argentina.
Fil: Jure Zupan. National Institute of Chemistry; Ljubljana. Slovenia.
Fil: Daniel Cicerone. Universidad Nacional de San Martín. Instituto de Investigación e Ingeniería Ambiental; Buenos Aires. Argentina.
Fil: Jorge Magallanes. Universidad Nacional de San Martín. Instituto de Investigación e Ingeniería Ambiental; Buenos Aires. Argentina.
description The problem of incomplete data matrices is repeatedly found in large databases; posing a significant obstacle for an effective treatment of data. This paper examines a self-organizing-map (SOM) based method of data imputation under the concept of distance object per one weight; to predict physicochemical parameters of water samples in a data set where concentrations of different analytes were missed. The method was evaluated according to two different possibilities: (a) including vectors of samples with and without missing data in the training data set and (b) pre-training a SOM for a data set with no missing values and then making imputations for a second data set (prediction set) of samples with missing values. Evaluations were made using a surface water data set of 270 samples from Reconquista River; in Buenos Aires Province; Argentina; by artificially setting a range of 17% to 39% of the data to missing. Results were compared to imputations made through professional criteria. SOMs gave reasonable estimates; with no statistically significant differences from estimates made through professional criteria; proving thus to be a suitable time-saving imputation method.
publishDate 2015
dc.date.none.fl_str_mv 2015-03
dc.type.none.fl_str_mv info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
status_str publishedVersion
format article
dc.identifier.none.fl_str_mv Folguera, L. et al (2015). Self-Organizing Maps for Imputation of Missing Data in Incomplete Data Matrices. En: Chemometrics and Intelligent Laboratory Systems. Elsevier Science 143, 146-151
0169-7439
https://ri.unsam.edu.ar/handle/123456789/1009
identifier_str_mv Folguera, L. et al (2015). Self-Organizing Maps for Imputation of Missing Data in Incomplete Data Matrices. En: Chemometrics and Intelligent Laboratory Systems. Elsevier Science 143, 146-151
0169-7439
url https://ri.unsam.edu.ar/handle/123456789/1009
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/restrictedAccess
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Atribución-NoComercial-CompartirIgual 2.5 Argentina (CC BY-NC-SA 2.5)
eu_rights_str_mv restrictedAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Atribución-NoComercial-CompartirIgual 2.5 Argentina (CC BY-NC-SA 2.5)
dc.format.none.fl_str_mv application/pdf
pp. 146-151
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science Bv
publisher.none.fl_str_mv Elsevier Science Bv
dc.source.none.fl_str_mv Chemometrics and Intelligent Laboratory Systems. 143: 146-151 (2015) Elsevier B.V.
http://dx.doi.org/10.1016/j.chemolab.2015.03.002
reponame:Repositorio Institucional (UNSAM)
instname:Universidad Nacional de General San Martín
reponame_str Repositorio Institucional (UNSAM)
collection Repositorio Institucional (UNSAM)
instname_str Universidad Nacional de General San Martín
repository.name.fl_str_mv Repositorio Institucional (UNSAM) - Universidad Nacional de General San Martín
repository.mail.fl_str_mv lpastran@unsam.edu.ar
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score 12.559606

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