Please use this identifier to cite or link to this item: https://ri.unsam.edu.ar/handle/123456789/1009
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dc.contributor.authorFolguera, Laura-
dc.contributor.authorZupan, Jure-
dc.contributor.authorCicerone, Daniel-
dc.contributor.authorMagallanes, Jorge-
dc.date.accessioned2019-10-31T21:38:24Z-
dc.date.available2019-10-31T21:38:24Z-
dc.date.issued2015-03-
dc.identifier.citationFolguera, 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-
dc.identifier.issn0169-7439-
dc.identifier.urihttps://ri.unsam.edu.ar/handle/123456789/1009-
dc.description.abstractThe 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.-
dc.formatapplication/pdf-
dc.format.extentpp. 146-151-
dc.language.isoeng-
dc.publisherElsevier Science Bv-
dc.rightsinfo:eu-repo/semantics/restrictedAccess-
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/-
dc.sourceChemometrics and Intelligent Laboratory Systems. 143: 146-151 (2015) Elsevier B.V.-
dc.source.urihttp://dx.doi.org/10.1016/j.chemolab.2015.03.002-
dc.subjectCHEMOMETRICS-
dc.subjectARTIFICIAL NEURAL NETWORK-
dc.subjectSELF-ORGANIZING MAPS-
dc.subjectMISSING DATA IMPUTATION-
dc.subjectENVIRONMENTAL DATA SET-
dc.subject.classificationCIENCIAS QUÍMICAS-
dc.subject.classificationCIENCIAS EXACTAS Y NATURALES-
dc.titleSelf-Organizing Maps for Imputation of Missing Data in Incomplete Data Matrices-
dc.rights.licenseCreative Commons Atribución-NoComercial-CompartirIgual 2.5 Argentina (CC BY-NC-SA 2.5)-
dc.description.versioninfo:eu-repo/semantics/publishedVersion-
dc.description.filiationFil: Laura Folguera. Universidad Nacional de San Martín. Instituto de Investigación e Ingeniería Ambiental; Buenos Aires. Argentina.-
dc.description.filiationFil: Jure Zupan. National Institute of Chemistry; Ljubljana. Slovenia.-
dc.description.filiationFil: Daniel Cicerone. Universidad Nacional de San Martín. Instituto de Investigación e Ingeniería Ambiental; Buenos Aires. Argentina.-
dc.description.filiationFil: Jorge Magallanes. Universidad Nacional de San Martín. Instituto de Investigación e Ingeniería Ambiental; Buenos Aires. Argentina.-
dc.type.openaireinfo:eu-repo/semantics/article-
dc.type.snrdinfo:ar-repo/semantics/artículo-
item.languageiso639-1en-
item.fulltextCon texto completo-
item.grantfulltextreserved-
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