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Business Failure Prediction Based on a Cost-Sensitive Extreme Gradient Boosting Machine

https://doi.org/10.1109/access.2022.3168857
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Outgoing claims: the papers this work cites, builds on, or argues with.

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Land use/land cover recognition in arid zone using A multi-dimensional multi-grained residual Forest☆
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Bankruptcy forecasting: An empirical comparison of AdaBoost and neural networks
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Cost-sensitive business failure prediction when misclassification costs are uncertain: A heterogeneous ensemble selection approach
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Geometric mean based boosting algorithm with over-sampling to resolve data imbalance problem for bankruptcy prediction
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inferredcites →https://doi.org/10.1109/tsmca.2012.2224338openalex-snapshot
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inferredcites →https://doi.org/10.1109/tsmcb.2006.875872openalex-snapshot
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inferredcites →https://doi.org/10.1145/2939672.2939785openalex-snapshot
XGBoost
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MetaCost
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A Novel Ensemble Learning Approach for Corporate Financial Distress Forecasting in Fashion and Textiles Supply Chains
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inferredcites →https://doi.org/10.32614/cran.package.xgboostopenalex-snapshot
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A Review of Financial Distress Prediction Models: Logistic Regression and Multivariate Discriminant Analysis

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