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Machine Learning Models for Data Quality Assessment

EasyChair Preprint no. 13213

17 pagesDate: May 7, 2024


Data quality assessment plays a critical role in ensuring the reliability and accuracy of data used in machine learning applications. Machine learning models have emerged as powerful tools for automating the process of data quality assessment. This abstract provides an overview of machine learning models for data quality assessment, highlighting their significance, methodologies, and applications.


The abstract begins by emphasizing the importance of data quality in the context of machine learning, where the performance and effectiveness of models heavily rely on the quality of input data. It outlines the various dimensions of data quality, including accuracy, completeness, consistency, timeliness, and validity, which serve as the foundation for assessing data quality.


The abstract then explores different types of machine learning models used for data quality assessment, including rule-based models, statistical models, machine learning models, and hybrid models that combine multiple approaches. Each category is described, along with examples, to provide a comprehensive understanding of their methodologies and capabilities.

Keyphrases: Accuracy, completeness, Compliance, Data Governance, Data Quality Models

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Edwin Frank},
  title = {Machine Learning Models for Data Quality Assessment},
  howpublished = {EasyChair Preprint no. 13213},

  year = {EasyChair, 2024}}
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