  In the era of big data, the utilization of credit-scoring models to determine
the credit risk of applicants accurately becomes a trend in the future. The
conventional machine learning on credit scoring data sets tends to have poor
classification for the minority class, which may bring huge commercial harm to
banks. In order to classify imbalanced data sets, we propose a new ensemble
algorithm, namely, Weighted-Hybrid-Sampling-Boost (WHSBoost). In data sampling,
we process the imbalanced data sets with weights by the Weighted-SMOTE method
and the Weighted-Under-Sampling method, and thus obtain a balanced training
sample data set with equal weight. In ensemble algorithm, each time we train
the base classifier, the balanced data set is given by the method above. In
order to verify the applicability and robustness of the WHSBoost algorithm, we
performed experiments on the simulation data sets, real benchmark data sets and
real credit scoring data sets, comparing WHSBoost with SMOTE, SMOTEBoost and
HSBoost based on SVM, BPNN, DT and KNN.
