Ensemble boosted trees with synthetic features generation in application to bankruptcy prediction

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A Cluster-Based Boosting Algorithm for Bankruptcy Prediction in a Highly Imbalanced Dataset (2018, positive): “Next, Zieba et al. [25] utilized eXtreme Gradient Boosting (XGBoost) for learning from synthetic features to predict bankruptcy.”
Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil (2021, methodology): “In this ensemble-type method, decision trees are employed to link input and output variables through recursive dual separations, while the boosting method is adopted to associate many individual models for improving the performance of the hybrid model [82].”
Oversampling Techniques for Bankruptcy Prediction: Novel Features from a Transaction Dataset (2018, neutral): “In 2016, a novel approach for bankruptcy prediction that mainly applies eXtreme Gradient Boosting (XGB) for learning an ensemble of decision trees was proposed in [32].”
A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring (2017, methodology): “The study of Zięba et al. (2016) also applied XGBoost in bankruptcy prediction.”
Prediction and Sensitivity Analysis of Bubble Dissolution Time in 3D Selective Laser Sintering Using Ensemble Decision Trees (2019, methodology): “variables to input variables by recursive binary splits whereas the boosting technique is used to combine many single models to improve the predictive capability of the ensemble models [49].”