ClueGAIN: Application of Transfer Learning On Generative Adversarial Imputation Nets (GAIN)

TL;DR

ClueGAIN enhances data imputation by integrating transfer learning into GAIN, improving performance on high missing rate datasets and enabling similarity measurement between datasets.

Contribution

This study introduces ClueGAIN, the first method to incorporate transfer learning into GAIN for better imputation and dataset similarity assessment.

Findings

Improved imputation accuracy on high missing rate datasets

Effective measurement of dataset similarity

Demonstrated advantages over traditional GAIN

Abstract

Many studies have attempted to solve the problem of missing data using various approaches. Among them, Generative Adversarial Imputation Nets (GAIN) was first used to impute data with Generative Adversarial Nets (GAN) and good results were obtained. Subsequent studies have attempted to combine various approaches to address some of its limitations. ClueGAIN is first proposed in this study, which introduces transfer learning into GAIN to solve the problem of poor imputation performance in high missing rate data sets. ClueGAIN can also be used to measure the similarity between data sets to explore their potential connections.