Article

MIDA: Multiple Imputation Using Denoising Autoencoders

Authors:

Lovedeep Gondara,

Ke WangAuthors Info & Claims

Advances in Knowledge Discovery and Data Mining: 22nd Pacific-Asia Conference, PAKDD 2018, Melbourne, VIC, Australia, June 3-6, 2018, Proceedings, Part III

Pages 260 - 272

https://doi.org/10.1007/978-3-319-93040-4_21

Published: 17 June 2018 Publication History

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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Cited By

Wang JZhang YWang KLin XZhang W(2024)Missing Data Imputation with Uncertainty-Driven NetworkProceedings of the ACM on Management of Data10.1145/36549202:3(1-25)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3654920
Paterakis GFafalios SCharonyktakis PChristophides VTsamardinos I(2024)Do We Really Need Imputation in AutoML Predictive Modeling?ACM Transactions on Knowledge Discovery from Data10.1145/364364318:6(1-64)Online publication date: 12-Apr-2024
https://dl.acm.org/doi/10.1145/3643643
Zhang WLi GTang JLi JTsung FSerra ESpezzano F(2024)Data Imputation from the Perspective of Graph Dirichlet EnergyProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679669(3237-3247)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679669
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Information & Contributors

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Published In

cover image Guide Proceedings

Advances in Knowledge Discovery and Data Mining: 22nd Pacific-Asia Conference, PAKDD 2018, Melbourne, VIC, Australia, June 3-6, 2018, Proceedings, Part III

Jun 2018

851 pages

ISBN:978-3-319-93039-8

DOI:10.1007/978-3-319-93040-4

Editors:
Dinh Phung
Deakin University, Geelong, Victoria, Australia
,
Vincent S. Tseng
National Chiao Tung University, Hsinchu City, Taiwan
,
Geoffrey I. Webb
Monash University, Clayton, Victoria, Australia
,
Bao Ho
Japan Advanced Institute of Science and Technology, Nomi, Ishikawa, Japan
,
Mohadeseh Ganji
University of Melbourne, Melbourne, Victoria, Australia
,
Lida Rashidi
University of Melbourne, Melbourne, Victoria, Australia

© Springer International Publishing AG, part of Springer Nature 2018.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 17 June 2018

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Cited By

Wang JZhang YWang KLin XZhang W(2024)Missing Data Imputation with Uncertainty-Driven NetworkProceedings of the ACM on Management of Data10.1145/36549202:3(1-25)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3654920
Paterakis GFafalios SCharonyktakis PChristophides VTsamardinos I(2024)Do We Really Need Imputation in AutoML Predictive Modeling?ACM Transactions on Knowledge Discovery from Data10.1145/364364318:6(1-64)Online publication date: 12-Apr-2024
https://dl.acm.org/doi/10.1145/3643643
Zhang WLi GTang JLi JTsung FSerra ESpezzano F(2024)Data Imputation from the Perspective of Graph Dirichlet EnergyProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679669(3237-3247)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679669
Al-taezi MWang YZhu PHu QAl-badwi A(2024)Improved generative adversarial network with deep metric learning for missing data imputationNeurocomputing10.1016/j.neucom.2023.127062570:COnline publication date: 12-Apr-2024
https://dl.acm.org/doi/10.1016/j.neucom.2023.127062
Li AFeng CXu SCheng Y(2024)Graph t-SNE multi-view autoencoder for joint clustering and completion of incomplete multi-view dataKnowledge-Based Systems10.1016/j.knosys.2023.111324284:COnline publication date: 25-Jan-2024
https://dl.acm.org/doi/10.1016/j.knosys.2023.111324
Campos SZamora JAllende H(2024)Block-wise imputation EM algorithm in multi-source scenario: ADNI casePattern Analysis & Applications10.1007/s10044-024-01268-x27:2Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s10044-024-01268-x
Fan YYu XWieser RMeakin DShaton AJaubert JFlottemesch RHowell MBraid JBruckman LFrench RWu Y(2023)Spatio-Temporal Denoising Graph Autoencoders with Data Augmentation for Photovoltaic Data ImputationProceedings of the ACM on Management of Data10.1145/35887301:1(1-19)Online publication date: 30-May-2023
https://dl.acm.org/doi/10.1145/3588730

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