Handling imperfection: A taxonomy for machine learning on data with data quality defects

URN zum Zitieren dieses Dokuments:: urn:nbn:de:bvb:355-epub-769086
DOI zum Zitieren dieses Dokuments:: 10.5283/epub.76908

Hagn, Michael ; Heinrich, Bernd

; Krapf, Thomas ; Schiller, Alexander

Lizenz: Creative Commons Namensnennung 4.0 International
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Veröffentlichungsdatum dieses Volltextes: 24 Jun 2025 05:57

Alternative Links zum Volltext:DOI Verlag

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In recent years, machine learning (ML) has become ubiquitous in sectors including transportation, security, health, and finance to analyze large amounts of data and support decision-making. However, real-world datasets used in ML often exhibit various data quality (DQ) defects that can significantly impair the performance and validity of ML models and thus also the decisions derived from them. ...

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