Gutenberg Open Science: Discriminative machine learning for maximal representative subsampling

Please use this identifier to cite or link to this item: http://doi.org/10.25358/openscience-9834

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DC Field	Value	Language
dc.contributor.author	Hauptmann, Tony	-
dc.contributor.author	Fellenz, Sophie	-
dc.contributor.author	Nathan, Laksan	-
dc.contributor.author	Tüscher, Oliver	-
dc.contributor.author	Kramer, Stefan	-
dc.date.accessioned	2023-12-20T11:28:44Z	-
dc.date.available	2023-12-20T11:28:44Z	-
dc.date.issued	2023	-
dc.identifier.uri	https://openscience.ub.uni-mainz.de/handle/20.500.12030/9852	-
dc.description.abstract	Biased population samples pose a prevalent problem in the social sciences. Therefore, we present two novel methods that are based on positive-unlabeled learning to mitigate bias. Both methods leverage auxiliary information from a representative data set and train machine learning classifiers to determine the sample weights. The first method, named maximum representative subsampling (MRS), uses a classifier to iteratively remove instances, by assigning a sample weight of 0, from the biased data set until it aligns with the representative one. The second method is a variant of MRS – Soft-MRS – that iteratively adapts sample weights instead of removing samples completely. To assess the effectiveness of our approach, we induced artificial bias in a public census data set and examined the corrected estimates. We compare the performance of our methods against existing techniques, evaluating the ability of sample weights created with Soft-MRS or MRS to minimize differences and improve downstream classification tasks. Lastly, we demonstrate the applicability of the proposed methods in a real-world study of resilience research, exploring the influence of resilience on voting behavior. Through our work, we address the issue of bias in social science, amongst others, and provide a versatile methodology for bias reduction based on machine learning. Based on our experiments, we recommend to use MRS for downstream classification tasks and Soft-MRS for downstream tasks where the relative bias of the dependent variable is relevant.	en_GB
dc.language.iso	eng	de
dc.rights	CC BY	*
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/	*
dc.subject.ddc	004 Informatik	de_DE
dc.subject.ddc	004 Data processing	en_GB
dc.title	Discriminative machine learning for maximal representative subsampling	en_GB
dc.type	Zeitschriftenaufsatz	de
dc.identifier.doi	http://doi.org/10.25358/openscience-9834	-
jgu.type.dinitype	article	en_GB
jgu.type.version	Published version	de
jgu.type.resource	Text	de
jgu.organisation.department	FB 08 Physik, Mathematik u. Informatik	de
jgu.organisation.number	7940	-
jgu.organisation.name	Johannes Gutenberg-Universität Mainz	-
jgu.rights.accessrights	openAccess	-
jgu.journal.title	Scientific reports	de
jgu.journal.volume	13	de
jgu.pages.alternative	20925	de
jgu.publisher.year	2023	-
jgu.publisher.name	Macmillan Publishers Limited, part of Springer Nature	de
jgu.publisher.place	London	de
jgu.publisher.issn	2045-2322	de
jgu.organisation.place	Mainz	-
jgu.subject.ddccode	004	de
jgu.publisher.doi	10.1038/s41598-023-48177-3	de
jgu.organisation.ror	https://ror.org/023b0x485	-
jgu.subject.dfg	Ingenieurwissenschaften	de
Appears in collections:	DFG-491381577-G

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