Abstract
Real-world datasets often have missing values, which hinders the use of a large number of machine learning (ML) estimators. To overcome this limitation in a data analysis pipeline, data points may be deleted in a data preprocessing stage. However, an alternative better solution is data imputation.
Several methods based on Artificial Neural Networks (ANN) have been recently proposed as successful alternatives to classical discriminative imputation methods. Amongst those ANN imputation methods are the ones that rely on Generative Adversarial Networks (GAN).
This paper presents three data imputation methods based on GAN: SGAIN, WSGAIN-CP and WSGAIN-GP. These methods were tested on datasets with different settings of missing values probabilities, where the values are missing completely at random (MCAR). The evaluation of the newly developed methods shows that they are equivalent or outperform competitive state-of-the-art imputation methods in different ways, either in terms of response time, the data imputation quality, or the accuracy of post-imputation tasks (e.g., prediction or classification).
M. G. Naik—The second author is a participant in the BIH Charité Digital Clinician Scientist Program funded by the Charité – Universitätsmedizin Berlin, the Berlin Institute of Health and the German Research Foundation (DFG).
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Notes
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The first author used synthetic data to develop and test ML models for the kidney disease pilot (see https://www.bigmedilytics.eu/pilot/kidney-disease/) of BigMedilytics (an EU-funded project supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 780495).
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Acknowledgements
The first author thanks the ALGORITMI research centre, Universidade do Minho, where he conducts part of his research as an external collaborator. The third author developed his work at ALGORITMI research centre, Universidade do Minho, supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020.
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Neves, D.T., Naik, M.G., Proença, A. (2021). SGAIN, WSGAIN-CP and WSGAIN-GP: Novel GAN Methods for Missing Data Imputation. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12742. Springer, Cham. https://doi.org/10.1007/978-3-030-77961-0_10
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