Tabular Data Augmentation Using Artificial Intelligence: A Systematic Review and Taxonomic Framework
Context: Tabular data predominate in machine learning applications; however, data scarcity, class imbalance, and privacy-related constraints often impair the model performance. Therefore, AI-centric data-synthesis techniques have been adopted to mitigate these challenges. Objective: To systematicall...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11098780/ |
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