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

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

Access to Document

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

OpenUrl availability

Full text

Cite this

@inbook{988a0d58d26f47e58c922f47a91a84f9,

title = "Data Quality Assessment for ML Decision-Making",

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.",

keywords = "Quality factors, Time series, Neural networks, Text recognition, Electrocardiogram based identification, Data analysis",

author = "Alexandra-{\c S}tefania Moloiu and Grigore Albeanu and Henrik Madsen and Florin Popen{\c t}iu-Vl{\u a}dicescu",

year = "2023",

doi = "10.1007/978-3-031-21232-1_8",

language = "English",

isbn = "978-3-031-21231-4",

pages = "163--178",

booktitle = "Applications in Reliability and Statistical Computing",

publisher = "Springer",

edition = "1",

}

TY - CHAP

T1 - Data Quality Assessment for ML Decision-Making

AU - Moloiu, Alexandra-Ştefania

AU - Albeanu, Grigore

AU - Madsen, Henrik

AU - Popenţiu-Vlădicescu, Florin

PY - 2023

Y1 - 2023

N2 - 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.

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

M3 - Book chapter

SN - 978-3-031-21231-4

SN - 978-3-031-21234-5

SP - 163

EP - 178

BT - Applications in Reliability and Statistical Computing

PB - Springer

ER -

Data Quality Assessment for ML Decision-Making

Abstract

Keywords

Access to Document

OpenUrl availability

Fingerprint

Cite this