  Missing data is one of the most common preprocessing problems. In this paper,
we experimentally research the use of generative and non-generative models for
feature reconstruction. Variational Autoencoder with Arbitrary Conditioning
(VAEAC) and Generative Adversarial Imputation Network (GAIN) were researched as
representatives of generative models, while the denoising autoencoder (DAE)
represented non-generative models. Performance of the models is compared to
traditional methods k-nearest neighbors (k-NN) and Multiple Imputation by
Chained Equations (MICE). Moreover, we introduce WGAIN as the Wasserstein
modification of GAIN, which turns out to be the best imputation model when the
degree of missingness is less than or equal to 30%. Experiments were performed
on real-world and artificial datasets with continuous features where different
percentages of features, varying from 10% to 50%, were missing. Evaluation of
algorithms was done by measuring the accuracy of the classification model
previously trained on the uncorrupted dataset. The results show that GAIN and
especially WGAIN are the best imputers regardless of the conditions. In
general, they outperform or are comparative to MICE, k-NN, DAE, and VAEAC.
