Support vector machines with evolutionary feature selection for default prediction

Wolfgang Härdle, Dedy Prastyo and Christian Hafner

No 2012-030, SFB 649 Discussion Papers from Humboldt University Berlin, Collaborative Research Center 649: Economic Risk

Abstract: Predicting default probabilities is at the core of credit risk management and is becoming more and more important for banks in order to measure their client's degree of risk, and for firms to operate successfully. The SVM with evolutionary feature selection is applied to the CreditReform database. We use classical methods such as discriminan analysis (DA), logit and probit models as benchmark On overall, GA-SVM is outperforms compared to the benchmark models in both training and testing dataset.

Keywords: SVM; Genetic algorithm; global optmimum; default prediction (search for similar items in EconPapers)
JEL-codes: C14 C45 C61 C63 G33 (search for similar items in EconPapers)
Date: 2012
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Working Paper: Support Vector Machines with Evolutionary Feature Selection for Default Prediction (2013)
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