A multi-distribution integrated WGAN model for effective data imputation

Missing values are a pervasive issue in real-world datasets, often degrading predictive performance and introducing computational instability. Existing imputation approaches typically focus on either data distribution modelling or attribute correlation analysis independently, limiting their ability to capture the underlying data structure comprehensively. Furthermore, most current methods are supervised and require complete datasets for training, restricting their applicability to fully missing or large-scale datasets. To overcome these limitations, this paper proposes kTails-WGAN, a novel unsupervised imputation framework based on Wasserstein Generative Adversarial Networks (WGANs). The proposed method incorporates data probability distributions into the discriminator to improve imputation accuracy and introduces a refined loss function to better preserve distributional consistency. Unlike conventional supervised approaches, kTails-WGAN does not require complete datasets during training, making it particularly suitable for datasets with extensive missing values. Experimental evaluations on benchmark datasets demonstrate that kTails-WGAN achieves competitive and, in many cases, superior performance compared with existing imputation methods, as measured by RMSE and AUC-ROC metrics.