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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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Principais autores: Ying Zhang, Li Deng, Bo Wei
Format: Artigo
Sprog:Inglês
Udgivet: MDPI AG 2024-05-01
Serier:Mathematics
Fag:
imbalanced data
feature standard deviation
oversampling strategy
Random-SMOTE
Online adgang:https://www.mdpi.com/2227-7390/12/11/1709
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