The random subspace binary logit (RSBL) model for bankruptcy prediction

This paper proposes the random subspace binary logit (RSBL) model (or random subspace binary logistic regression analysis) by taking the random subspace approach and using the classical logit model to generate a group of diverse logit decision agents from various perspectives for predictive problem. These diverse logit models are then combined for a more accurate analysis. The proposed RSBL model takes advantage of both logit (or logistic regression) and random subspace approaches. The random subspace approach generates diverse sets of variables to represent the current problem as different masks. Different logit decision agents from these masks, instead of a single logit model, are constructed. To verify its performance, we used the proposed RSBL model to forecast corporate failure in China. The results indicate that this model significantly improves the predictive ability of classical statistical models such as multivariate discriminant analysis, logit model, and probit model. Thus, the proposed model should make logit model more suitable for predictive problems in academic and industrial uses.