Genetic algorithm-based heuristic for feature selection in credit risk assessment

  title={Genetic algorithm-based heuristic for feature selection in credit risk assessment},
  author={Stjepan Oreski and Goran Oreski},
  journal={Expert Syst. Appl.},
In this paper, an advanced novel heuristic algorithm is presented, the hybrid genetic algorithm with neural networks (HGA-NN), which is used to identify an optimum feature subset and to increase the classification accuracy and scalability in credit risk assessment. This algorithm is based on the following basic hypothesis: the high-dimensional input feature space can be preliminarily restricted to only the important features. In this preliminary restriction, fast algorithms for feature ranking… CONTINUE READING
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Research design and statistical analysis

  • J. L. Myers, A. Well
  • 2003
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  • K. Bache, M. Lichman
  • 2013
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