Predicting feature imputability in the absence of ground truth

• URL: http://arxiv.org/abs/2007.07052v1
• Date: Tue, 14 Jul 2020 14:24:07 GMT
• Title: Predicting feature imputability in the absence of ground truth
• Authors: Niamh McCombe, Xuemei Ding, Girijesh Prasad, David P. Finn, Stephen Todd, Paula L. McClean, KongFatt Wong-Lin
• Abstract summary: It is difficult to evaluate whether data has been imputed accurately (lack of ground truth) in real life applications. This paper proposes an effective and simple principal component based method for determining whether individual data features can be accurately imputed.
• Score: 2.7684432804249477
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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