Study of corporate credit risk prediction based on integrating boosting and random subspace

With the rapid growth and increased competition in credit industry, the corporate credit risk prediction is becoming more important for credit-granting institutions. In this paper, we propose an integrated ensemble approach, called RS-Boosting, which is based on two popular ensemble strategies, i.e., boosting and random subspace, for corporate credit risk prediction. As there are two different factors encouraging diversity in RS-Boosting, it would be advantageous to get better performance. Two corporate credit datasets are selected to demonstrate the effectiveness and feasibility of the proposed method. Experimental results reveal that RS-Boosting gets the best performance among seven methods, i.e., logistic regression analysis (LRA), decision tree (DT), artificial neural network (ANN), bagging, boosting and random subspace. All these results illustrate that RS-Boosting can be used as an alternative method for corporate credit risk prediction.