An experimental comparison of classification algorithms for imbalanced credit scoring data sets

@article{Brown2012AnEC,
  title={An experimental comparison of classification algorithms for imbalanced credit scoring data sets},
  author={Iain Brown and Christophe Mues},
  journal={Expert Syst. Appl.},
  year={2012},
  volume={39},
  pages={3446-3453}
}
In this paper, we set out to compare several techniques that can be used in the analysis of imbalanced credit scoring data sets. In a credit scoring context, imbalanced data sets frequently occur as the number of defaulting loans in a portfolio is usually much lower than the number of observations that do not default. As well as using traditional classification techniques such as logistic regression, neural networks and decision trees, this paper will also explore the suitability of gradient… CONTINUE READING

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