A Xgboost Risk Model Via Feature Selection and Bayesian Hyper-Parameter Optimization
A Xgboost Risk Model Via Feature Selection and Bayesian Hyper-Parameter Optimization Author Name Yan Wang and Xuelei Sherry Ni, Kennesaw State University, USA Abstract This paper aims to explore models based on the extreme gradient boosting (XGBoost) approach for business risk classification. Feature selection (FS) algorithms and hyper-parameter optimizations are simultaneously considered during model training. The five most commonly used FS methods including weight by Gini, weight by Chi-square, hierarchical variable clustering, weight by correlation, and weight by information are applied to alleviate the effect of redundant features. Two hyper-parameter optimization approaches, random search (RS) and Bayesian tree-structuredParzen Estimator (TPE), are applied in XGBoost. The effect of different FS and hyper-parameter optimization methods on the model performance are investigated by the Wilcoxon Signed Rank Test. The performance of XGBoost is compared to the traditionally utilize...
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