Support Vector Machines with Evolutionary Feature Selection for Default Prediction
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References listed on IDEAS
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CitationsCitations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
- Dedy Dwi Prastyo & Wolfgang Karl HÃ¤rdle, 2014. "Localising Forward Intensities for Multiperiod Corporate Default," SFB 649 Discussion Papers SFB649DP2014-040, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
- Wolfgang Karl HÃ¤rdle & Dedy Dwi Prastyo, 2013. "Default Risk Calculation based on Predictor Selection for the Southeast Asian Industry," SFB 649 Discussion Papers SFB649DP2013-037, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
More about this item
KeywordsSVM; Genetic algorithm; global optmimum; default prediction;
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
- C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
- C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
- G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
NEP fieldsThis paper has been announced in the following NEP Reports:
- NEP-CMP-2012-05-02 (Computational Economics)
- NEP-FOR-2012-05-02 (Forecasting)
- NEP-RMG-2012-05-02 (Risk Management)
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