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1. The impact of data quality in machine learning
The main types of error to be considered when assessing the quality of a dataset are: missing values, outliers, inconsistent values (i.e. values that do not satisfy a set of predefined constraints), and finally, duplicates, as illustrated in the table 1 .
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The impact of data quality in machine learning
Detection and correction by machine learning
Bibliography
Events
International conferences :
Very Large Databases (VLDB) Conference: http://vldb.org/conference.html
ACM SIGMOD (Special Interest Group on Management of Data): https://dl.acm.org/event.cfm?id=RE227
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Standards and norms
- Data quality — Part 1: Overview https://www.iso.org/standard/50798.html - ISO/TS 8000-1 - 2011
- Data quality — Part 2: Vocabulary https://www.iso.org/standard/73456.html - ISO 8000-2 - 2017
- Data quality — Part 8: Information and data quality: Concepts and measuring https://www.iso.org/standard/60805.html - ISO 8000-8 - 2015
- Data quality — Part 61: Data quality management: Process reference model...
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