Improving Missing Data Imputation with Deep Generative Models

Abstract

Datasets with missing values are very common on industry applications, and they can have a negative impact on machine learning models. Recent studies introduced solutions to the problem of imputing missing values based on deep generative models. Previous experiments with Generative Adversarial Networks and Variational Autoencoders showed interesting results in this domain, but it is not clear which method is preferable for different use cases. The goal of this work is twofold: we present a comparison between missing data imputation solutions based on deep generative models, and we propose improvements over those methodologies. We run our experiments using known real life datasets with different characteristics, removing values at random and reconstructing them with several imputation techniques. Our results show that the presence or absence of categorical variables can alter the selection of the best model, and that some models are more stable than others after similar runs with different random number generator seeds.

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• A review of Generative Adversarial Networks for Electronic Health Records: applications, evaluation measures and data sources
• A Review of Deep Learning Methods for Irregularly Sampled Medical Time Series Data
• Astroinformatics based search for globular clusters in the Fornax Deep Survey
• Multiple Imputation for Biomedical Data using Monte Carlo Dropout Autoencoders
• Minority Class Oversampling for Tabular Data with Deep Generative Models