Data Quality Assessment for ML Decision-Making

Alexandra-Ştefania Moloiu; Grigore Albeanu; Henrik Madsen; Florin Popenţiu-Vlădicescu

doi:10.1007/978-3-031-21232-1_8

Data Quality Assessment for ML Decision-Making

Alexandra-Ştefania Moloiu
, Grigore Albeanu
, Henrik Madsen
, Florin Popenţiu-Vlădicescu

Spiru Haret University
National University of Science and Technology POLITEHNICA Bucharest
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Research output: Chapter in Book/Report/Conference proceeding › Book chapter › Research › peer-review

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.

Original language	English
Title of host publication	Applications in Reliability and Statistical Computing
Publisher	Springer
Publication date	2023
Edition	1
Pages	163-178
ISBN (Print)	978-3-031-21231-4, 978-3-031-21234-5
ISBN (Electronic)	978-3-031-21232-1
DOIs	https://doi.org/10.1007/978-3-031-21232-1_8
Publication status	Published - 2023

Keywords

Quality factors
Time series
Neural networks
Text recognition
Electrocardiogram based identification
Data analysis

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10.1007/978-3-031-21232-1_8

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AU - Moloiu, Alexandra-Ştefania

AU - Albeanu, Grigore

AU - Madsen, Henrik

AU - Popenţiu-Vlădicescu, Florin

PY - 2023

Y1 - 2023

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AB - 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.

KW - Quality factors

KW - Time series

KW - Neural networks

KW - Text recognition

KW - Electrocardiogram based identification

KW - Data analysis

U2 - 10.1007/978-3-031-21232-1_8

DO - 10.1007/978-3-031-21232-1_8

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BT - Applications in Reliability and Statistical Computing

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ER -

Data Quality Assessment for ML Decision-Making

Abstract

Keywords

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