Improved generative adversarial imputation networks for missing data

Abstract

Conventional statistical methods for missing data imputation have been challenging to adapt to the large-scale new features of high dimensionality. Moreover, the missing data imputation methods based on Generative Adversarial Networks (GAN) are plagued with gradient vanishing and mode collapse. To address these problems, we have proposed a new imputation method based on GAN to enhance the accuracy of missing data imputation in this study. We refer to our missing data method using Generative Adversarial Imputation Networks (MGAIN). Specifically, the least squares loss is first introduced to solve the gradient vanishing problem and ensure the high quality of the output data in MGAIN. To mitigate mode collapse, dual discriminator is used in the model, which improved the diversity of output data to avoid the degradation of computational performance caused by single data. As a result, MGAIN generates rich and accurate imputation values. The MGAIN enhances imputation accuracy and reduces the root mean square error metric by 21.66% compared to the baseline model. We evaluated our method on baseline datasets and found that MGAIN outperformed state-of-the-art and popular imputation methods, demonstrating its effectiveness and superiority.