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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Mohameden, Ahmed
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Computer Science & IT Engineering

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A survey of missing data imputation techniques: statistical methods, machine learning models, and GAN-based approaches Sadegh, Rifaa; Mohameden, Ahmed; Salihi, Mohamed Lemine; Nanne, Mohamedade Farouk
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp2876-2888

Abstract

Efficiently addressing missing data is critical in data analysis across diverse domains. This study evaluates traditional statistical, machine learning, and generative adversarial network (GAN)-based imputation methods, emphasizing their strengths, limitations, and applicability to different data types and missing data mechanisms (missing completely at random (MCAR), missing at random (MAR), missing not at random (MNAR)). GAN-based models, including generative adversarial imputation network (GAIN), view imputation generative adversarial network (VIGAN), and SolarGAN, are highlighted for their adaptability and effectiveness in handling complex datasets, such as images and time series. Despite challenges like computational demands, GANs outperform conventional methods in capturing non-linear dependencies. Future work includes optimizing GAN architectures for broader data types and exploring hybrid models to enhance imputation accuracy and scalability in real-world applications.
Co-Authors Nanne, Mohamedade Farouk Sadegh, Rifaa Salihi, Mohamed Lemine