ML Anthology

Predicting Feature Imputability in the Absence of Ground Truth

Niamh McCombe, Xuemei Ding, Girijesh Prasad, David P Finn, Stephen Todd, Paula L McClean, Kongfatt Wong-Lin
ICMLW 2020
/icmlw/2020/mccombe2020icmlw-predicting/

Abstract

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 relationship between principal component loadings and feature imputability, even in the presence of extreme missingness and lack of ground truth. This work will have important implications in practical data imputation strategies.

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Cite

Text

McCombe et al. "Predicting Feature Imputability in the Absence of Ground Truth." ICML 2020 Workshops: Artemiss, 2020.

Markdown

[McCombe et al. "Predicting Feature Imputability in the Absence of Ground Truth." ICML 2020 Workshops: Artemiss, 2020.](https://mlanthology.org/icmlw/2020/mccombe2020icmlw-predicting/)

BibTeX

@inproceedings{mccombe2020icmlw-predicting,
  title     = {{Predicting Feature Imputability in the Absence of Ground Truth}},
  author    = {McCombe, Niamh and Ding, Xuemei and Prasad, Girijesh and Finn, David P and Todd, Stephen and McClean, Paula L and Wong-Lin, Kongfatt},
  booktitle = {ICML 2020 Workshops: Artemiss},
  year      = {2020},
  url       = {https://mlanthology.org/icmlw/2020/mccombe2020icmlw-predicting/}
}