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Discriminative machine learning for maximal representative subsampling.
Hauptmann, Tony; Fellenz, Sophie; Nathan, Laksan; Tüscher, Oliver; Kramer, Stefan.
Afiliação
  • Hauptmann T; Institute of Computer Science, Johannes Gutenberg University Mainz, Mainz, Germany. thauptmann@uni-mainz.de.
  • Fellenz S; Institute of Computer Science, Johannes Gutenberg University Mainz, Mainz, Germany.
  • Nathan L; Institute of Computer Science, Johannes Gutenberg University Mainz, Mainz, Germany.
  • Tüscher O; The Leibniz Institute for Resilience Research, Mainz, Germany.
  • Kramer S; Department of Psychiatry and Psychotherapy, University Medical Center Mainz, Mainz, Germany.
Sci Rep ; 13(1): 20925, 2023 Nov 27.
Article em En | MEDLINE | ID: mdl-38017053
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.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Sci Rep Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Sci Rep Ano de publicação: 2023 Tipo de documento: Article