Reviewing Autoencoders for Missing Data Imputation: Technical Trends, Applications and Outcomes

• Type: Article in International Scientific Journal
• Date: 2020

Authors

Pereira, RC (Author) Other; Santos, MS (Author) Other; Pedro Pereira Rodrigues (Author) FMUP; Pedro Henriques Abreu (Author) Other

Journal

The Journal is awaiting validation by the Administrative Services.

Title: JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH

Vol. 69

Pages: 1255-1285

ISSN: 1076-9757

Indexing

ISI Web of Knowledge - 43 Citations

Scopus - 56 Citations

Other information

Authenticus ID: P-00T-AHQ

DOI: 10.1613/jair.1.12312

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 31