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We propose a two-stage hybrid approach with neural networks as the newfeature construction algorithms for bankcard response classifications. Thehybrid model uses a very simple neural network structure as the new featureconstruction tool in the first stage, then the newly created features are usedas the additional input variables in logistic regression in the second stage.The model is compared with the traditional one-stage model in credit customerresponse classification. It is observed that the proposed two-stage modeloutperforms the one-stage model in terms of accuracy, the area under ROC curve,and KS statistic. By creating new features with the neural network technique,the underlying nonlinear relationships between variables are identified.Furthermore, by using a very simple neural network structure, the model couldovercome the drawbacks of neural networks in terms of its long training time,complex topology, and limited interpretability.

A two-stage hybrid model by using artificial neural networks as feature construction algorithms