A survey on dataset quality in machine learning
Open accessAbstract
With the rise of big data, the quality of datasets has become a crucial factor affecting the performance of machine learning models. High-quality datasets are essential for the realization of data value. This survey article summarizes the research direction of dataset quality in machine learning, including the definition of related concepts, analysis of quality issues and risks, and a review of dataset quality dimensions and metrics throughout the dataset lifecycle and a review of dataset quality metrics analyzed from a dataset lifecycle perspective and summarized in literatures. Furthermore, this article introduces a comprehensive quality evaluation process, which includes a framework for dataset quality evaluation with dimensions and metrics, computation methods for quality metrics, and assessment models. These studies provide valuable guidance for evaluating dataset quality in the field of machine learning, which can help improve the accuracy, efficiency, and generalization ability of machine learning models, and promote the development and application of artificial intelligence technology.
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The availability and quality of these datasets are important for developing models that can generalize well across different scenarios encountered in public transport.
“The availability and quality of these datasets are important for developing models that can generalize well across different scenarios encountered in public transport [95].”
From A Review of Passenger Counting in Public Transport Concepts with Solution Proposal Based on Image Processing and Machine Learning (2024) · cites this paper as [95]
The utilization of a large dataset significantly improves the performance of the models in classifying either cyberbullying or non-cyberbullying texts.
“The utilization of a large dataset significantly improves the performance of the models in classifying either cyberbullying or non-cyberbullying texts [85,149].”
From Cyberbullying detection: Exploring datasets, technologies, and approaches on social media platforms (2026) · cites this paper as [149]
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- Authors
- Gong, Y., Liu, G., Xue, Y., Li, R., & Meng, L.
- Affiliations
- Chinese Academy of Sciences (CN)
- Corresponding author
- Meng, Lingzhong
- Venue
- Information and Software Technology
- Publisher
- Elsevier BV
- Published
- 2023-10
- Volume
- 162
- Pages
- 107268
- ISSN
- 0950-5849 (ISSN-L)
- OpenAlex
- W4378978494
- Semantic Scholar
- 818028e8b75e9b7a4043daf553cbb3e64f78161d
- Cited by
- 354
- References
- 60
- Citations by year
- 2026: 136 · 2025: 145 · 2024: 67 · 2023: 6
- FWCI
- 65.17
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Information and Software Technology (publishedVersion, cc-by-nc-nd)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Data Quality and Management
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Physical Sciences › Computer Science › Information Systems › Research Data Management Practices
- Keywords
- dataset quality, quality assessment framework, quality dimensions, quality risk, big data, machine learning data
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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