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- W3126720980 abstract "Abstract Financial distress prediction provides an effective warning system for banks and investors to correctly guide decisions on granting credit. Ensemble methods have demonstrated their performance in corporate failure prediction. Among the ensemble methods, gradient boosting has been successfully used in bankruptcy prediction. In this paper, we propose a novel approach to classify categorical data using gradient boosting decision trees, namely, CatBoost. First, we investigate the importance of the features identified by the CatBoost model. Second, we compare our approach with eight reference machine learning models at one, two and three years before failure. Our model demonstrates an effective improvement in the power of classification performance compared with other advanced approaches." @default.
- W3126720980 created "2021-02-15" @default.
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- W3126720980 date "2021-05-01" @default.
- W3126720980 modified "2023-09-28" @default.
- W3126720980 title "CatBoost model and artificial intelligence techniques for corporate failure prediction" @default.
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- W3126720980 doi "https://doi.org/10.1016/j.techfore.2021.120658" @default.
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