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dc.contributor.authorMat Jasin, A.
dc.contributor.authorNeagu, Daniel
dc.contributor.authorCsenki, Attila
dc.date.accessioned2018-10-08T11:42:22Z
dc.date.available2018-10-08T11:42:22Z
dc.date.issued2018-07
dc.identifier.citationMat Jasin A, Neagu D and Csenki A (2018) The wild bootstrap resampling in regression imputation algorithm with a Gaussian Mixture Model. In: Perner P (Ed.) Machine Learning and Data Mining in Pattern Recognition. MLDM 2018. Lecture Notes in Computer Science, vol 10935. Springer, Cham: 219-230.en_US
dc.identifier.urihttp://hdl.handle.net/10454/16593
dc.descriptionYesen_US
dc.description.abstractUnsupervised learning of finite Gaussian mixture model (FGMM) is used to learn the distribution of population data. This paper proposes the use of the wild bootstrapping to create the variability of the imputed data in single miss-ing data imputation. We compare the performance and accuracy of the proposed method in single imputation and multiple imputation from the R-package Amelia II using RMSE, R-squared, MAE and MAPE. The proposed method shows better performance when compared with the multiple imputation (MI) which is indeed known as the golden method of missing data imputation techniques.en_US
dc.language.isoenen_US
dc.relation.isreferencedbyhttps://link.springer.com/chapter/10.1007/978-3-319-96133-0_17#citeasen_US
dc.rights© Springer International Publishing AG, part of Springer Nature 2018. Reproduced in accordance with the publisher's self-archiving policy.en_US
dc.subjectMissing data imputationen_US
dc.subjectGaussian Mixture Modelen_US
dc.subjectBootstrapen_US
dc.titleThe wild bootstrap resampling in regression imputation algorithm with a Gaussian Mixture Modelen_US
dc.status.refereedYesen_US
dc.date.Accepted2018-07-15
dc.date.application2018-07-08
dc.typeConference paperen_US
dc.date.EndofEmbargo2019-07-09
dc.type.versionAccepted Manuscripten_US
dc.description.publicnotesThe full-text of this article will be released for public view at the end of the publisher embargo on 9 July 2019.en_US
refterms.dateFOA2018-10-08T11:42:25Z


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