Missing Features Reconstruction Using a Wasserstein Generative Adversarial Imputation Network
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F20%3A00341700" target="_blank" >RIV/68407700:21240/20:00341700 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007%2F978-3-030-50423-6_17" target="_blank" >https://link.springer.com/chapter/10.1007%2F978-3-030-50423-6_17</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-50423-6_17" target="_blank" >10.1007/978-3-030-50423-6_17</a>
Alternative languages
Result language
angličtina
Original language name
Missing Features Reconstruction Using a Wasserstein Generative Adversarial Imputation Network
Original language description
Missing data is one of the most common preprocessing problems. In this paper, we experimentally research the use of generative and non-generative models for feature reconstruction. Variational Autoencoder with Arbitrary Conditioning (VAEAC) and Generative Adversarial Imputation Network (GAIN) were researched as representatives of generative models, while the denoising autoencoder (DAE) represented non-generative models. Performance of the models is compared to traditional methods k-nearest neighbors (k-NN) and Multiple Imputation by Chained Equations (MICE). Moreover, we introduce WGAIN as the Wasserstein modification of GAIN, which turns out to be the best imputation model when the degree of missingness is less than or equal to 30%. Experiments were performed on real-world and artificial datasets with continuous features where different percentages of features, varying from 10% to 50%, were missing. Evaluation of algorithms was done by measuring the accuracy of the classification model previously trained on the uncorrupted dataset. The results show that GAIN and especially WGAIN are the best imputers regardless of the conditions. In general, they outperform or are comparative to MICE, k-NN, DAE, and VAEAC.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA18-18080S" target="_blank" >GA18-18080S: Fusion-Based Knowledge Discovery in Human Activity Data</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Computational Science - ICCS 2020
ISBN
978-3-030-50422-9
ISSN
0302-9743
e-ISSN
1611-3349
Number of pages
15
Pages from-to
225-239
Publisher name
Springer
Place of publication
Cham
Event location
Amsterdam
Event date
Jun 3, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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