An experimental comparison of ensemble of classifiers for bankruptcy prediction and credit scoring
In this paper, we investigate the performance of several systems based on ensemble of classifiers for bankruptcy prediction and credit scoring. The obtained results are very encouraging, our results improved the performance obtained using the stand-alone classifiers. We show that the method Random Subspace outperforms the other ensemble methods tested in this paper. Moreover, the best stand-alone method is the multi-layer perceptron neural net, while the best method tested in this work is the Random Subspace of LevenbergMarquardt neural net. In this work, three financial datasets are chosen for the experiments: Australian credit, German credit, and Japanese credit.
ensemble of classifiers bankruptcy prediction credit scoring