Addressing imbalance in health datasets: A new method NR-clustering SMOTE and distance metric modification
An imbalanced dataset often challenges machine learning, particularly classification methods. Underrepresented minority classes can result in biased and inaccurate models. The Synthetic Minority Over-Sampling Technique (SMOTE) was developed to address the problem of imbalanced data. Over time, sever...
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| Main Authors: | , , , |
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| 格式: | Article |
| 語言: | English |
| 出版: |
Tech Science Press
2025
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| 主題: | |
| 在線閱讀: | http://umpir.ump.edu.my/id/eprint/44043/1/Addressing%20imbalance%20in%20health%20datasets.pdf http://umpir.ump.edu.my/id/eprint/44043/ https://doi.org/10.32604/cmc.2024.060837 https://doi.org/10.32604/cmc.2024.060837 |
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