A Two-Stage Hybrid Model by Using Artificial Neural Networks as Feature Construction Algorithms

Yan Wang1, Xuelei Sherry Ni1 and Brian Stone2, 1Kennesaw State University, USA and 2Atlanticus Services Corporation, USA

ABSTRACT

We propose a two-stage hybrid approach with neural networks as the new feature construction algorithms for bankcard response classifications. The hybrid model uses a very simpleneural network structure as the new feature construction tool in the firststage, thenthe newly created features are used asthe additional input variables in logistic regression in the second stage. The modelis compared with the traditional onestage model in credit customer response classification. It is observed that the proposed two-stage model outperforms the one-stage model in terms of accuracy, the area under ROC curve, andKS statistic. By creating new features with theneural network technique, the underlying nonlinear relationships between variables are identified. Furthermore, by using a verysimple neural network structure, the model could overcome the drawbacks of neural networks interms of its long training time, complex topology, and limited interpretability.

KEYWORDS

Hybrid Model, Neural Network, Feature Construction, Logistic Regression, Bankcard Response Model

ORIGINAL SOURCE URL: https://aircconline.com/ijdkp/V8N6/8618ijdkp01.pdf

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