An Efficient Generative Data Imputation Toolbox with Adversarial Learning

Yangyang Wu, Xiaoye Miao, Zilinghan Li, Shilan He, Xinkai Yuan, Jianwei Yin

Abstract: The dramatically increasing volume of incomplete data makes the imputation models computationally infeasible in many real-life applications. In this demonstration, we propose a scalable and extendible data imputation toolbox, SEMI, to deal with large-scale incomplete data imputation efficiently and visually. SEMI consists of three modules: data preprocessing, data imputation, and post-imputation prediction. It is built upon SCIS, a scalable imputation system, to significantly speed up the training of generative adversarial imputation models under accuracy-guarantees for large-scale incomplete data. Using a public real-world large-scale incomplete weather dataset, we demonstrate that, SEMI is capable of assisting users to efficiently address real-life large-scale imputation issues, from the aspects of high-efficient imputation system, user-friendly performance visualization, and easy-to-use interaction operation.