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- W4313003997 abstract "As the global economic environment has become more complicated in recent years, more and more credit bonds have defaulted. The credit risk early warning model plays a very effective role in preventing and controlling financial risk and debt default. This paper uses machine learning methods to establish a credit default risk prediction framework. In this paper, the oversampling technique is first applied to deal with imbalanced credit default data sets and then the credit risk detection performance of several machine learning algorithms is compared. The empirical results show that the performance of the ensemble learning algorithms is the best." @default.
- W4313003997 created "2023-01-05" @default.
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- W4313003997 date "2022-01-01" @default.
- W4313003997 modified "2023-10-16" @default.
- W4313003997 title "Credit risk detection based on machine learning algorithms" @default.
- W4313003997 doi "https://doi.org/10.1504/ijfsm.2022.126871" @default.
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