Benchmarking Robust Machine Learning Models Under Data Imperfections in Real-World Data Science Scenarios
Machine learning systems deployed in real-world environments frequently encounter data imperfections such as noise, missing values, class imbalance, and distribution shifts. Despite substantial progress in model development, most evaluation protocols rely on clean benchmark datasets, creating a gap...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | en en |
| Published: |
INTI International University
2026
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| Subjects: | |
| Online Access: | http://eprints.intimal.edu.my/2300/2/853 http://eprints.intimal.edu.my/2300/3/jods2026_03.pdf http://eprints.intimal.edu.my/2300/ http://ipublishing.intimal.edu.my/jods.html |
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