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2018 | OriginalPaper | Chapter

MIDA: Multiple Imputation Using Denoising Autoencoders

Authors : Lovedeep Gondara, Ke Wang

Published in: Advances in Knowledge Discovery and Data Mining

Publisher: Springer International Publishing

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Abstract

Missing data is a significant problem impacting all domains. State-of-the-art framework for minimizing missing data bias is multiple imputation, for which the choice of an imputation model remains nontrivial. We propose a multiple imputation model based on overcomplete deep denoising autoencoders. Our proposed model is capable of handling different data types, missingness patterns, missingness proportions and distributions. Evaluation on several real life datasets show our proposed model significantly outperforms current state-of-the-art methods under varying conditions while simultaneously improving end of the line analytics.

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Title: MIDA: Multiple Imputation Using Denoising Autoencoders
Authors: Lovedeep Gondara
Ke Wang
Publisher: Springer International Publishing
Book: Advances in Knowledge Discovery and Data Mining
Print ISBN: 978-3-319-93039-8

Electronic ISBN: 978-3-319-93040-4

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-319-93040-4_21

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