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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Bibliographic Details

Published in:	Mathematics
Main Authors:	Ying Zhang, Li Deng, Bo Wei
Format:	Article
Language:	English
Published:	MDPI AG 2024-05-01
Subjects:	imbalanced data feature standard deviation oversampling strategy Random-SMOTE
Online Access:	https://www.mdpi.com/2227-7390/12/11/1709