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- W4229447118 abstract "To improve the ability of market to avoid and prevent credit risk and strengthen the awareness of market risk early warning, SMOTE is used to process the unbalanced sample, and fruit fly optimization algorithm (FOA) is utilized to optimize the parameters of support vector machine (SVM), and thus an improved SVM market risk early warning model is proposed. The simulation results show that the proposed model has excellent stability and generalization ability, and it can predict market credit risk accurately. Compared with the prediction model based on FOA-SMOTE-BP and FOA-SMOTE-Logit, the proposed model performs better on the indicators of G value, F value, and AUC value, which provides a reference for market credit risk prediction." @default.
- W4229447118 created "2022-05-11" @default.
- W4229447118 creator A5081224375 @default.
- W4229447118 date "2022-05-09" @default.
- W4229447118 modified "2023-09-26" @default.
- W4229447118 title "Market Risk Early Warning Based on Deep Learning and Fruit Fly Optimization" @default.
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- W4229447118 doi "https://doi.org/10.1155/2022/4844856" @default.
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