Multiple Imputation by Generative Adversarial Networks for Classification with Incomplete Data

Missing values present as the most common problem in real-world data science. Inadequate treatment of missing values could often result in mass errors. Hence missing values should be managed conscientiously for classification. Generative Adversarial Networks (GANs) have been applied for imputing missing values in most recent years. This paper proposes a multiple imputation method to estimate missing values for classification through the integration of GAN and ensemble learning. Our propose method MIGAN utilises GAN to generate different training observations which are then used to conduct ensemble classifiers for classification with missing data. We conducted our experiments examine MIGAN on various data sets as well as comparing MIGAN with the state-of-the-art imputation methods. The experimental results show significant results, which highlights the accuracy of MIGAN in classifying the missing data.