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Data Quality Assessment for ML Decision-Making

In: Applications in Reliability and Statistical Computing

Author

Listed:
  • Alexandra-Ştefania Moloiu

    (TypingDNA)

  • Grigore Albeanu

    (“Spiru Haret” University)

  • Henrik Madsen

    (Danish Technical University)

  • Florin Popenţiu-Vlădicescu

    (University “Politehnica” of Bucharest & Academy of Romanian Scientists)

Abstract

Data quality has a strong effect on the design, validation and testing of decision-making systems. New paradigms of future models in the knowledge society need to analyze clean, complete, consistent, and high-quality data. This paper presents three case studies from different fields in which models are constructed using machine learning strategies. Projects on text recognition, electrocardiogram-based identification and data analysis are described in relation to input data quality and system performance.

Suggested Citation

  • Alexandra-Ştefania Moloiu & Grigore Albeanu & Henrik Madsen & Florin Popenţiu-Vlădicescu, 2023. "Data Quality Assessment for ML Decision-Making," Springer Series in Reliability Engineering, in: Hoang Pham (ed.), Applications in Reliability and Statistical Computing, pages 163-178, Springer.
    • Handle: RePEc:spr:ssrchp:978-3-031-21232-1_8
    DOI: 10.1007/978-3-031-21232-1_8
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