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Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Credit scoring has gained increasing attentions from banks, which can benefit from reducing possible risks of default. Based on the analysis of relationship between the performance of ensemble model and that of base classifiers, this paper proposes a selective neural network ensemble model for credit scoring, In which Artificial neural networks and ensemble learning methods are firstly employed to build a base classifiers pool, then hierarchical clustering algorithm is used to divide those base classifiers into several clusters, then the classifiers with highest accuracy in each cluster are chose to vote for the final decision. Three real world credit datasets are selected as the experimental data to demonstrate the accuracy of the model. The results show that selective neural network ensemble model can significantly improved the efficiency in selection of base classifiers and generalization ability and thereby show enough attractive features for credit risk management system.