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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-21964
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dc.contributor.authorTempl, Matthias-
dc.date.accessioned2021-03-12T13:13:16Z-
dc.date.available2021-03-12T13:13:16Z-
dc.date.issued2020-12-18-
dc.identifier.otherarXiv:2012.10300v1de_CH
dc.identifier.urihttps://arxiv.org/abs/2012.10300de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/21964-
dc.description.abstractMethods of deep learning have become increasingly popular in recent years, but they have not arrived in compositional data analysis. Imputation methods for compositional data are typically applied on additive, centered or isometric log-ratio representations of the data. Generally, methods for compositional data analysis can only be applied to observed positive entries in a data matrix. Therefore one tries to impute missing values or measurements that were below a detection limit. In this paper, a new method for imputing rounded zeros based on artificial neural networks is shown and compared with conventional methods. We are also interested in the question whether for ANNs, a representation of the data in log-ratios for imputation purposes, is relevant. It can be shown, that ANNs are competitive or even performing better when imputing rounded zeros of data sets with moderate size. They deliver better results when data sets are big. Also, we can see that log-ratio transformations within the artificial neural network imputation procedure nevertheless help to improve the results. This proves that the theory of compositional data analysis and the fulfillment of all properties of compositional data analysis is still very important in the age of deep learning.de_CH
dc.format.extent26de_CH
dc.language.isoende_CH
dc.publisherarXivde_CH
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0/de_CH
dc.subjectDeep learningde_CH
dc.subjectArtificial neural networksde_CH
dc.subjectCompositional datade_CH
dc.subjectRounded zerosde_CH
dc.subjectImputationde_CH
dc.subjectReplacementde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleArtificial neural networks to impute rounded zeros in compositional datade_CH
dc.typeWorking Paper – Gutachten – Studiede_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Datenanalyse und Prozessdesign (IDP)de_CH
dc.identifier.doi10.21256/zhaw-21964-
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.webfeedStatistik und Quantitative Financede_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Templ, M. (2020). Artificial neural networks to impute rounded zeros in compositional data. arXiv. https://doi.org/10.21256/zhaw-21964
Templ, M. (2020) Artificial neural networks to impute rounded zeros in compositional data. arXiv. Available at: https://doi.org/10.21256/zhaw-21964.
M. Templ, “Artificial neural networks to impute rounded zeros in compositional data,” arXiv, Dec. 2020. doi: 10.21256/zhaw-21964.
TEMPL, Matthias, 2020. Artificial neural networks to impute rounded zeros in compositional data [online]. arXiv. Verfügbar unter: https://arxiv.org/abs/2012.10300
Templ, Matthias. 2020. “Artificial Neural Networks to Impute Rounded Zeros in Compositional Data.” arXiv. https://doi.org/10.21256/zhaw-21964.
Templ, Matthias. Artificial Neural Networks to Impute Rounded Zeros in Compositional Data. arXiv, 18 Dec. 2020, https://doi.org/10.21256/zhaw-21964.


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