Oct 28, 2022 · Extensive experimental results show that incorporating synthetic samples with training data achieves better performance compared to the baseline ...
We then used three state-of-the-art imputation models that can handle mixed data: MissForest, multivariate imputation by chained equations, and denoising auto ...
Imputation in mixed datasets that contain both numerical and categorical attributes is challenging and has received little attention. Machine learning-based ...
Extensive experimental results show that incorporating synthetic samples with training data achieves better performance compared to the baseline methods for ...
Apr 20, 2021 · GAIN showed better accuracy as an imputation method for missing data in large real-world clinical datasets compared to MICE and missForest.
We propose a novel method for imputing missing data by adapting the well-known Generative Ad- versarial Nets (GAN) framework. Accordingly, we call our method ...
A missing time-series data interpolation method based on random forest and a generative adversarial interpolation network is proposed.
Missing: Mixed | Show results with:Mixed
We first used generative adversarial network (GAN) methods to increase the amount of training data. We considered two state-of-the-art GANs (tabular and ...
Dec 21, 2021 · In this work, we develop MI-GAN, a valid imputation method that works on the MAR (and MCAR) mechanism with the theoretical support. There are ...
This thesis aims to provide valuable insights into the potential of using generative adversarial networks to solve the missing data problem in mixed-type ...