Evolutionary Generative Adversarial Network for Missing Data Imputation

Abstract

Generative adversarial networks (GAN) have been a compelling method for generating new data in data science industry. This generative model has been accepted for data imputation in specific areas. However, existing GANs (GAN and its variants) are likely to suffer from training problems such as instability and mode collapse. This paper proposes a new novel method for imputing missing data by adapting GAN and Evolutionary Computation framework. Therefore, the new methods is named Evolutionary Generative Adversarial for Imputation Data (EGAIN). EGAIN utilises the different training observations with mutation, selection, and evolving process among a population of generator G. In this experiment, three different loss functions is used to validate the output of G and the training process of discriminator D. EGAIN is also tested on various datasets and is compared with state-of-the-art imputation method for illustrating its performance.