Predicting feature imputability in the absence of ground truth
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.
Keywords
Cite
@article{arxiv.2007.07052,
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 preprint arXiv:2007.07052},
year = {2020}
}
Comments
5 pages, 3 figures, 1 table. In: Proceedings of the 37th International Conference on Machine Learning (ICML), 2020