Hybrid credit scoring model using neighborhood rough set and multi-layer ensemble classification

Credit scoring is a procedure to estimate the risk related with credit products which is calculated using applicants’ credentials and applicants’ historical data. However, the data may have some redundant and irrelevant information and features, which lead to lower accuracy on the credit scoring model. So, by eliminating the redundant features can resolve the problem of credit scoring dataset. In this work, we have proposed a hybrid credit scoring model based on dimensionality reduction by Neighborhood Rough Set (NRS) algorithm and layered ensemble classification with weighted voting approach to improve the classification performance. For classifiers’ raking, we have proposed a novel classifier ranking algorithm as an underlying model for representing ranks of the classifiers based on classifier accuracy. It is used on seven heterogeneous classifiers for finding the ranks of those classifiers. Further five best ranking classifiers are used as base classifier in layered ensemble framework. Results of the ensemble frameworks (Majority Voting (MV), Weighted Voting (WV), Layered Majority Voting (LMV), Layered Weighted Voting (LWV)) with all features and after feature reduction by various existing feature selection algorithms are compared in terms of accuracy, sensitivity, specificity and G-measure. Further, results of ensemble frameworks with NRS are also compared in terms of ROC curve analysis. The experimental outcomes reveal the success of proposed methods in two benchmarked credit scoring (Australian credit scoring and German loan approval) datasets obtained from UCI repository.