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Missing data imputation in multivariate data by evolutionary algorithms

dc.contributor.authorFigueroa García, Juan Carlos
dc.contributor.authorKalenatic, Dusko
dc.contributor.authorLópez Bello, Cesar Amilcar
dc.date.accessioned8/28/2019 14:29
dc.date.available8/28/2019 14:29
dc.date.issued2011-09
dc.identifier.issn0747-5632
dc.identifier.otherhttps://www.sciencedirect.com/science/article/pii/S0747563210003080
dc.identifier.urihttp://hdl.handle.net/10818/36915
dc.description7 páginases_CO
dc.description.abstractThis paper presents a proposal based on an evolutionary algorithm to impute missing observations in multivariate data. A genetic algorithm based on the minimization of an error function derived from their covariance matrix and vector of means is presented. All methodological aspects of the genetic structure are presented. An extended explanation of the design of the fitness function is provided. An application example is solved by the proposed method.en
dc.formatapplication/pdfes_CO
dc.language.isoenges_CO
dc.publisherComputers in Human Behaviores_CO
dc.relation.ispartofseriesComputers in Human Behavior Volume 27, Issue 5, September 2011, Pages 1468-1474
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.sourceUniversidad de La Sabanaes_CO
dc.sourceIntellectum Repositorio Universidad de La Sabanaes_CO
dc.subjectMissing dataen
dc.subjectEvolutionary optimizationen
dc.subjectMultivariate analysisen
dc.subjectMultiple data imputationen
dc.titleMissing data imputation in multivariate data by evolutionary algorithmsen
dc.typejournal articlees_CO
dc.type.hasVersionpublishedVersiones_CO
dc.rights.accessRightsopenAccesses_CO
dc.identifier.doi10.1016/j.chb.2010.06.026


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