Establishing strong imputation performance of a denoising autoencoder in a wide range of missing data problems
This work addresses missing data problems in data analysis, offering an incremental improvement over existing imputation methods.
The paper tackled missing data imputation by developing a denoising autoencoder framework, which achieved the smallest error across varying corruption levels in experiments with single and multi-type variable datasets.
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.