Multi-Output Regression with Generative Adversarial Networks (MOR-GANs)
File(s)applsci-12-09209-v2.pdf (3.24 MB)
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Author(s)
Type
Journal Article
Abstract
Regression modelling has always been a key process in unlocking the relationships between independent and dependent variables that are held within data. In recent years, machine learning has uncovered new insights in many fields, providing predictions to previously unsolved problems. Generative Adversarial Networks (GANs) have been widely applied to image processing producing good results, however, these methods have not often been applied to non-image data. Seeing the powerful generative capabilities of the GANs, we explore their use, here, as a regression method. In particular, we explore the use of the Wasserstein GAN (WGAN) as a multi-output regression method. The resulting method we call Multi-Output Regression GANs (MOR-GANs) and its performance is compared to a Gaussian Process Regression method (GPR) - a commonly used non-parametric regression method that has been well tested on small datasets with noisy responses. The WGAN regression model performs well for all types of datasets and exhibits substantial improvements over the performance of the GPR for certain types of datasets, demonstrating the flexibility of the GAN as a model for regression.
Date Issued
2022-09-14
Date Acceptance
2022-09-08
ISSN
2076-3417
Publisher
MDPI AG
Start Page
1
End Page
24
Journal / Book Title
Applied Sciences
Volume
12
Issue
18
Copyright Statement
© 2022 by the authors.
Submitted to Appl. Sci. for
possible open access publication
under the terms and conditions
of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Submitted to Appl. Sci. for
possible open access publication
under the terms and conditions
of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
License URI
Sponsor
Engineering & Physical Science Research Council (E
Engineering & Physical Science Research Council (E
Engineering & Physical Science Research Council (EPSRC)
Engineering & Physical Science Research Council (EPSRC)
Engineering & Physical Science Research Council (E
Identifier
https://www.mdpi.com/2076-3417/12/18/9209
Grant Number
RG80519
EP/P033180/1
EP/T003189/1
EP/T000414/1
EP/V036777/1 20319369
Publication Status
Published
Date Publish Online
2022-09-14