GADIN: Generative Adversarial Denoise Imputation Network for Incomplete Data

Dong Li, Zhicong Liu, Mingfeng Hu, Baoyan Song, Xiaohuan Shan

Abstract: Data imputation has increasingly gained attention due to its critical role in enhancing data quality and accuracy. However, traditional imputation methods often lack the ability to leverage the underlying category information and employ static denoising strategies, leading to suboptimal results. In this paper, we propose a novel data imputation method based on generative adversarial denoise network to predict and fill in the missing values. Our approach first employs a dataset partitioning scheme to divide the dataset into several subsets based on potential data categories. We then propose a Generative Adversarial Denoise Imputation Network (GAD IN) to combine dynamic noise reduction with generative adversarial networks to enhance the model's adaptability and robustness. Extensive experiments on real-world datasets validate the superior performance of our proposed method in comparison to existing techniques.

External IDs:dblp:conf/icdm/LiLHSS24