# A Survey on Data Cleaning Methods for Improved Machine Learning Model Performance

arXiv: https://arxiv.org/abs/2109.07127 · HTML: https://mlsanity.com/paper/2109.07127/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Sep 2021.

## Built on by (most cited first, 7 of 7)

- [CoDA: Agentic Systems for Collaborative Data Visualization](https://mlsanity.com/paper/2510.03194/) (2025)
- [REIN: A Comprehensive Benchmark Framework for Data Cleaning Methods in ML Pipelines](https://mlsanity.com/paper/2302.04702/) (2023)
- [Sanitizing Manufacturing Dataset Labels Using Vision-Language Models](https://mlsanity.com/paper/2506.23465/) (2025)
- [CDR-Bench: Evaluating Faithful Execution of Compositional, Order-Sensitive Data Refinement Recipes](https://mlsanity.com/paper/2606.31435/) (2026)
- [Deep Learning based pipeline for anomaly detection and quality enhancement in industrial binder jetting processes](https://mlsanity.com/paper/2209.10178/) (2022)
- [NER-to-MRC: Named-Entity Recognition Completely Solving as Machine Reading Comprehension](https://mlsanity.com/paper/2305.03970/) (2023)
- [Filtering out mislabeled training instances using black-box optimization and quantum annealing](https://mlsanity.com/paper/2501.06916/) (2025)

## Abstract (arXiv)

Data cleaning is the initial stage of any machine learning project and is one of the most critical processes in data analysis. It is a critical step in ensuring that the dataset is devoid of incorrect or erroneous data. It can be done manually with data wrangling tools, or it can be completed automatically with a computer program. Data cleaning entails a slew of procedures that, once done, make the data ready for analysis. Given its significance in numerous fields, there is a growing interest in the development of efficient and effective data cleaning frameworks. In this survey, some of the most recent advancements of data cleaning approaches are examined for their effectiveness and the future research directions are suggested to close the gap in each of the methods.