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

URN to cite this document:: urn:nbn:de:bvb:355-epub-769086
DOI to cite this document:: 10.5283/epub.76908

Hagn, Michael ; Heinrich, Bernd

; Krapf, Thomas ; Schiller, Alexander

License: Creative Commons Attribution 4.0
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Date of publication of this fulltext: 24 Jun 2025 05:57

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Abstract

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. ...