Multi-Output Regression with Generative Adversarial Networks (MOR-GANs)

• Published in: Applied sciences 2022-09, Vol.12 (18), p.9209
• Main Authors: Phillips, Toby R. F., Heaney, Claire E., Benmoufok, Ellyess, Li, Qingyang, Hua, Lily, Porter, Alexandra E., Chung, Kian Fan, Pain, Christopher C.
• Format: Article
• Language: English
• Subjects: Algorithms; Datasets; Dependent variables; Gaussian process; Generative Adversarial Networks; Image processing; Independent variables; Machine learning; Methods; multi-modal distributions; multi-output regression; Neural networks; Random variables; regression; Time series; Wasserstein GAN
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app12189209

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.