Hybrid genetic algorithms and support vector machines for bankruptcy prediction

Bankruptcy prediction is an important and widely studied topic since it can have significant impact on bank lending decisions and profitability. Recently, the support vector machine (SVM) has been applied to the problem of bankruptcy prediction. The SVM-based method has been compared with other methods such as the neural network (NN) and logistic regression, and has shown good results. The genetic algorithm (GA) has been increasingly applied in conjunction with other AI techniques such as NN and Case-based reasoning (CBR). However, few studies have dealt with the integration of GA and SVM, though there is a great potential for useful applications in this area. This study proposes methods for improving SVM performance in two aspects: feature subset selection and parameter optimization. GA is used to optimize both a feature subset and parameters of SVM simultaneously for bankruptcy prediction.