Abstract
Missing data is an issue often addressed with imputation strategies that replace the missing values with plausible ones. A trend in these strategies is the use of generative models, one being Variational Autoencoders. However, the default loss function of this method gives the same importance to all data, while a more suitable solution should focus on the missing values. In this work an extension of this method with a custom loss function is introduced (Variational Autoencoder with Weighted Loss). The method was compared with state-of-the-art generative models and the results showed improvements higher than 40% in several settings. © ESANN 2020 - Proceedings, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.

Abstract

Abstract
Missing data is a problem often found in real-world datasets and it can degrade the performance of most machine learning models. Several deep learning techniques have been used to address this issue, and one of them is the Autoencoder and its Denoising and Variational variants. These models are able to learn a representation of the data with missing values and generate plausible new ones to replace them. This study surveys the use of Autoencoders for the imputation of tabular data and considers 26 works published between 2014 and 2020. The analysis is mainly focused on discussing patterns and recommendations for the architecture, hyperparameters and training settings of the network, while providing a detailed discussion of the results obtained by Autoencoders when compared to other state-of-the-art methods, and of the data contexts where they have been applied. The conclusions include a set of recommendations for the technical settings of the network, and show that Denoising Autoencoders outperform their competitors, particularly the often used statistical methods.

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Abstract