SGAIN, WSGAIN-CP and WSGAIN-GP: Novel GAN Methods for Missing Data Imputation

Abstract

Real-world datasets often have missing values, which hinders the use of a large number of machine learning (ML) estimators. To overcome this limitation in a data analysis pipeline, data points may be deleted in a data preprocessing stage. However, an alternative better solution is data imputation.Several methods based on Artificial Neural Networks (ANN) have been recently proposed as successful alternatives to classical discriminative imputation methods. Amongst those ANN imputation methods are the ones that rely on Generative Adversarial Networks (GAN).This paper presents three data imputation methods based on GAN: SGAIN, WSGAIN-CP and WSGAIN-GP. These methods were tested on datasets with different settings of missing values probabilities, where the values are missing completely at random (MCAR). The evaluation of the newly developed methods shows that they are equivalent or outperform competitive state-of-the-art imputation methods in different ways, either in terms of response time, the data imputation quality, or the accuracy of post-imputation tasks (e.g., prediction or classification).