Please use this identifier to cite or link to this item: https://hdl.handle.net/10316/101583
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dc.contributor.authorSantos, Miriam Seoane-
dc.contributor.authorPereira, Ricardo Cardoso-
dc.contributor.authorCosta, Adriana Fonseca-
dc.contributor.authorSoares, Jastin Pompeu-
dc.contributor.authorSantos, Joao-
dc.contributor.authorAbreu, Pedro Henriques-
dc.date.accessioned2022-09-01T08:47:56Z-
dc.date.available2022-09-01T08:47:56Z-
dc.date.issued2019-
dc.identifier.issn2169-3536pt
dc.identifier.urihttps://hdl.handle.net/10316/101583-
dc.description.abstractThe performance evaluation of imputation algorithms often involves the generation of missing values. Missing values can be inserted in only one feature (univariate con guration) or in several features (multivariate con guration) at different percentages (missing rates) and according to distinct missing mechanisms, namely, missing completely at random, missing at random, and missing not at random. Since the missing data generation process de nes the basis for the imputation experiments (con guration, missing rate, and missing mechanism), it is essential that it is appropriately applied; otherwise, conclusions derived from ill-de ned setups may be invalid. The goal of this paper is to review the different approaches to synthetic missing data generation found in the literature and discuss their practical details, elaborating on their strengths and weaknesses. Our analysis revealed that creating missing at random and missing not at random scenarios in datasets comprising qualitative features is the most challenging issue in the related work and, therefore, should be the focus of future work in the field.pt
dc.language.isoengpt
dc.relationNORTE-01-0145-FEDER-000027pt
dc.relationFCT - SFRH/BD/138749/2018pt
dc.rightsopenAccesspt
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt
dc.subjectData preprocessingpt
dc.subjectmissing datapt
dc.subjectmissing data generationpt
dc.subjectmissing data mechanismspt
dc.titleGenerating Synthetic Missing Data: A Review by Missing Mechanismpt
dc.typearticle-
degois.publication.firstPage11651pt
degois.publication.lastPage11667pt
degois.publication.titleIEEE Accesspt
dc.peerreviewedyespt
dc.identifier.doi10.1109/ACCESS.2019.2891360pt
degois.publication.volume7pt
dc.date.embargo2019-01-01*
uc.date.periodoEmbargo0pt
item.cerifentitytypePublications-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextopen-
item.languageiso639-1en-
item.fulltextCom Texto completo-
item.openairetypearticle-
crisitem.author.orcid0000-0002-9278-8194-
Appears in Collections:I&D CISUC - Artigos em Revistas Internacionais
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