  Dealing with missing data in data analysis is inevitable. Although powerful
imputation methods that address this problem exist, there is still much room
for improvement. In this study, we examined single imputation based on deep
autoencoders, motivated by the apparent success of deep learning to efficiently
extract useful dataset features. We have developed a consistent framework for
both training and imputation. Moreover, we benchmarked the results against
state-of-the-art imputation methods on different data sizes and
characteristics. The work was not limited to the one-type variable dataset; we
also imputed missing data with multi-type variables, e.g., a combination of
binary, categorical, and continuous attributes. To evaluate the imputation
methods, we randomly corrupted the complete data, with varying degrees of
corruption, and then compared the imputed and original values. In all
experiments, the developed autoencoder obtained the smallest error for all
ranges of initial data corruption.
