• Corpus ID: 220514317

Predicting feature imputability in the absence of ground truth

@article{Mccombe2020PredictingFI,
  title={Predicting feature imputability in the absence of ground truth},
  author={Niamh Mccombe and Xuemei Ding and Girijesh Prasad and David P. Finn and Stephen Todd and Paula L. McClean and KongFatt Wong-Lin},
  journal={ArXiv},
  year={2020},
  volume={abs/2007.07052}
}
Data imputation is the most popular method of dealing with missing values, but in most real life applications, large missing data can occur and it is difficult or impossible to evaluate whether data has been imputed accurately (lack of ground truth). This paper addresses these issues by proposing an effective and simple principal component based method for determining whether individual data features can be accurately imputed - feature imputability. In particular, we establish a strong linear… 

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