Bankruptcy Prediction Using Multiple Classifier System with Mutual Information Feature Grouping

The prediction of bankruptcy helps an organization to choose its business partners and banks to approve or reject loan requests. So, it is essential to predict the bankruptcy of an organization. In this work, a multiple classifier system which combines decision from several different base classifiers trained using different samples with different input features is proposed for the bankruptcy prediction. The input features for each base classifier are selected using its mutual information with respect to the output. Experimental results of the proposed method using a real bankruptcy dataset from Compustat Global Dataset are promising.