A novel outlier-adapted multi-stage ensemble model with feature transformation for credit scoring

Three datasets are chosen from the UCI machine learning
repository in this study, which have been extensively adopted in data-driven
researches, including Australian and Japanese datasets (Asuncion & Newman,
2007), and Polish bankruptcy dataset (Zięba et al., 2016).

The three datasets contain different numbers of samples
and features. Each sample in a credit dataset can be classified into good
credit or bad credit. The size of
Australian credit dataset is 690, with 307 samples in good credit and 383 in
bad, and its feature dimension is 14, with 6 numerical and 8 categorical
features. The size of Japanese credit dataset is 690, with 307 samples in good
credit and 383 in bad, and its feature dimension is 15, with 6 numerical and 9 categorical
features. Similarly, there are 7027 samples in Polish bankruptcy dataset, with
6756 samples in good credit and 271 in bad, and its 64 input features are numerical.
All the dimensions of the input features of the three datasets listed in Table
1 do not include the class labels.