Hybrid system with genetic algorithm and artificial neural networks and its application to retail credit risk assessment

@article{Oreski2012HybridSW,
  title={Hybrid system with genetic algorithm and artificial neural networks and its application to retail credit risk assessment},
  author={Stjepan Oreski and Dijana Oreski and Goran Oreski},
  journal={Expert Syst. Appl.},
  year={2012},
  volume={39},
  pages={12605-12617}
}
The databases of the banks around the world have accumulated large quantities of information about clients and their financial and payment history. These databases can be used for the credit risk assessment, but they are commonly high dimensional. Irrelevant features in a training dataset may produce less accurate results of classification analysis. Data preprocessing is required to prepare the data for classification to increase the predictive accuracy. Feature selection is a preprocessing… CONTINUE READING
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References

Publications referenced by this paper.
Showing 1-10 of 20 references

Evaluating consumer loans using neural networks. Omega

  • R. Malhotra, D. K. Malhotra
  • 2003
Highly Influential
8 Excerpts

International convergence of capital measurement and capital standards: A revised framework

  • BIS
  • Basel Committee of Banking Supervision, Bank for…
  • 2006
Highly Influential
7 Excerpts

Mining: Concepts and techniques (2nd ed.)

  • J. Han, M. Kamber
  • 2006
Highly Influential
5 Excerpts

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