UAFS: Uncertainty-Aware Feature Selection for Problems with Missing Data

TL;DR

This paper introduces UAFS, a feature selection method that accounts for uncertainty due to missing data, improving imputation accuracy and subsequent predictive modeling in high-dimensional, incomplete datasets.

Contribution

The paper presents a novel uncertainty-aware feature selection method that enhances imputation and prediction accuracy in datasets with missing data, supported by theoretical and empirical validation.

Findings

UAFS improves imputation accuracy across various datasets.

Using UAFS leads to better predictive performance.

The method is compatible with multiple imputation techniques.

Abstract

Missing data are a concern in many real world data sets and imputation methods are often needed to estimate the values of missing data, but data sets with excessive missingness and high dimensionality challenge most approaches to imputation. Here we show that appropriate feature selection can be an effective preprocessing step for imputation, allowing for more accurate imputation and subsequent model predictions. The key feature of this preprocessing is that it incorporates uncertainty: by accounting for uncertainty due to missingness when selecting features we can reduce the degree of missingness while also limiting the number of uninformative features being used to make predictive models. We introduce a method to perform uncertainty-aware feature selection (UAFS), provide a theoretical motivation, and test UAFS on both real and synthetic problems, demonstrating that across a variety of data sets and levels of missingness we can improve the accuracy of imputations. Improved imputation due to UAFS also results in improved prediction accuracy when performing supervised learning using these imputed data sets. Our UAFS method is general and can be fruitfully coupled with a variety of imputation methods.