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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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書誌詳細
主要な著者:	Hairani, Hairani, Widiyaningtyas, Triyanna, Prasetya, Didik Dwi, Afrig, Aminuddin
フォーマット:	論文
言語:	English
出版事項:	Tech Science Press 2025
主題:	QA75 Electronic computers. Computer science
オンライン･アクセス:	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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