mlsanityData to 7 Oct 2026

Data Cleaning and Machine Learning: A Systematic Literature Review

arXiv 2310.01765 ↗First posted 3 Oct 2023cs.LG cs.DBcited by 9

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Abstract (arXiv)

Context: Machine Learning (ML) is integrated into a growing number of systems for various applications. Because the performance of an ML model is highly dependent on the quality of the data it has been trained on, there is a growing interest in approaches to detect and repair data errors (i.e., data cleaning). Researchers are also exploring how ML can be used for data cleaning; hence creating a dual relationship between ML and data cleaning. To the best of our knowledge, there is no study that comprehensively reviews this relationship. Objective: This paper's objectives are twofold. First, it aims to summarize the latest approaches for data cleaning for ML and ML for data cleaning. Second, it provides future work recommendations. Method: We conduct a systematic literature review of the papers published between 2016 and 2022 inclusively. We identify different types of data cleaning activities with and for ML: feature cleaning, label cleaning, entity matching, outlier detection, imputation, and holistic data cleaning. Results: We summarize the content of 101 papers covering various data cleaning activities and provide 24 future work recommendations. Our review highlights many promising data cleaning techniques that can be further extended. Conclusion: We believe that our review of the literature will help the community develop better approaches to clean data.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 87 of its 228 references that are in the map. First posted on arXiv 3 Oct 2023.

Builds on

87 of 228 references.

References · 87

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38 more
  1. 2017Understanding Black-box Predictions via Influence Functionscited by 1,714
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  22. 2017HoloClean: Holistic Data Repairs with Probabilistic Inferencecited by 79
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  24. 2019Data Cleansing for Models Trained with SGDcited by 67
  25. 2018Generative Adversarial Active Learning for Unsupervised Outlier Detectioncited by 65
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  31. 2021Interactive Label Cleaning with Example-based Explanationscited by 48
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  33. 2021Active label cleaning for improved dataset quality under resource constraintscited by 44
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50 most cited of 87 All 87 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 5.

9 papers.

  1. 2025LM-mixup: Text Data Augmentation via Language Model based Mixupcited by 3
  2. 2025CleanSurvival: Automated data preprocessing for time-to-event models using reinforcement learningcited by 2
  3. 2024Mislabeled examples detection viewed as probing machine learning models: concepts, survey and extensive benchmarkcited by 1
  4. 2026Remote Sensing Change Detection via Weak Temporal Supervisioncited by 1
  5. 2025Representation-Based Data Quality Audits for Audiocited by 0
  6. 2026QuAIL: Quality-Aware Inertial Learning for Robust Training under Data Corruptioncited by 0
  7. 2026Finding Most Influential Setscited by 0
  8. 2026Trustworthy Self-Composable Big-Data-as-a-Service: An LLM-Orchestrated Multi-Agent Framework for Automated Data Engineering, AutoML, MLOps Deployment, and Drift-Aware Lifecycle Optimizationcited by 0
  9. 2026Unsupervised Anomaly Detection for Image Dataset Quality Assurance in Multi-Center Breast MRIcited by 0