Categorical Imputation

Categorical imputation addresses the challenge of replacing missing values in categorical data, aiming to improve the accuracy and reliability of subsequent analyses. Current research focuses on developing advanced imputation methods, including those based on item response theory, graph neural networks, and generative adversarial networks (GANs), often incorporating deep learning architectures to capture complex data relationships. These improved techniques are crucial for various applications, enhancing the utility of datasets affected by missing data in fields ranging from social sciences and healthcare to environmental modeling and sensor data analysis. The ultimate goal is to create more robust and accurate analyses by mitigating the biases and uncertainties introduced by missing information.