Corpus ID: 237513734

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

@article{Lee2021ASO,
  title={A Survey on Data Cleaning Methods for Improved Machine Learning Model Performance},
  author={Ga Young Lee and Lubna Alzamil and Bakhtiyar Doskenov and Arash Termehchy},
  journal={ArXiv},
  year={2021},
  volume={abs/2109.07127}
}
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… Expand

References

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TLDR
This work proposes ActiveClean, a progressive framework for training Machine Learning models with data cleaning, which updates a model iteratively as the analyst cleans small batches of data, and includes numerous optimizations such as importance weighting and dirty data detection. Expand
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TLDR
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TLDR
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The Staggering Impact of Dirty Data
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Data Cleaning