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	A study on improving turnover intention forecasting by solving imbalanced data problems: focusing on SMOTE and generative adversarial networks 期刊论文 Journal of Big Data,2023, 卷号: 10, 期号: 1 作者: Park, Jungryeol; Kwon, Sundong; Jeong, Seon Phil 收藏 \| 浏览/下载：32/0 \| 提交时间：2024/01/22 Generative adversarial networks Imbalanced data SMOTE Turnover intention
	Imbalanced classification: A paradigm-based review 期刊论文 Statistical Analysis and Data Mining,2021, 卷号: 14, 期号: 5, 页码: 383-406 作者: Feng, Yang; Zhou, Min; Tong, Xin 收藏 \| 浏览/下载：35/0 \| 提交时间：2021/10/19 binary classification classical classification (CC) paradigm cost-sensitive (CS) learning paradigm imbalance ratio imbalanced data Neyman–Pearson (NP) paradigm resampling methods