FragmGAN: generative adversarial nets for fragmentary data imputation and prediction

Abstract

Modern scientific research and applications very often encounter ‘fragmentary data’ which brings big challenges to imputation and prediction. By leveraging the structure of response patterns, we propose a unified and flexible framework based on Generative Adversarial Nets (GAN) to deal with fragmentary data imputation and label prediction at the same time. Unlike most of the other generative model based imputation methods that either have no theoretical guarantee or only consider Missing Completed At Random (MCAR), the proposed FragmGAN has theoretical guarantees for imputation with data Missing At Random (MAR) while no hint mechanism is needed. FragmGAN trains a predictor with the generator and discriminator simultaneously. This linkage mechanism shows significant advantages for predictive performances in extensive experiments.

Keywords:

• Data integration
• fragmentary data
• generative adversarial nets
• imputation
• missing at random

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The authors gratefully acknowledge the research support from the National Key R&D Program of China [Grant Numbers 2021YFA1000100 and 2021YFA1000101], National Natural Science Foundation of China [Grant Numbers 72331005, 12071143 and 11831008] and the Basic Research Project of Shanghai Science and Technology Commission [Grant Number 22JC1400800].