A hybrid system with filter approach and multiple population genetic algorithm for feature selection in credit scoring

DOI10.1016/j.cam.2017.04.036zbMath1377.62201OpenAlexW2616541616MaRDI QIDQ1675394

Peng Zhang

Publication date: 27 October 2017

Published in: Journal of Computational and Applied Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cam.2017.04.036

zbMATH Keywords

hybrid approachfeature selectioncredit scoringHMPGA

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Applications of statistics to actuarial sciences and financial mathematics (62P05) Credit risk (91G40)

Related Items (3)

Forecasting volatility returns of oil price using gene expression programming approach. ⋮ Integrating data augmentation and hybrid feature selection for small sample credit risk assessment with high dimensionality ⋮ Feature selection based on distance correlation: a filter algorithm

Uses Software

• Matlab
• C4.5

Cites Work

• A genetic algorithm for the flexible job-shop scheduling problem
• Advances in Intelligent Data Analysis VI
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