Imbalanced Data Classification Based on Improved Random-SMOTE and Feature Standard Deviation
Oversampling techniques are widely used to rebalance imbalanced datasets. However, most of the oversampling methods may introduce noise and fuzzy boundaries for dataset classification, leading to the overfitting phenomenon. To solve this problem, we propose a new method (FSDR-SMOTE) based on Random-...
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| 主要な著者: | , , |
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| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
MDPI AG
2024-05-01
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| シリーズ: | Mathematics |
| 主題: | |
| オンライン･アクセス: | https://www.mdpi.com/2227-7390/12/11/1709 |
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