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

TL;DR

This paper introduces a two-stage hybrid model combining neural networks for feature construction and logistic regression for credit response classification, improving accuracy and interpretability over traditional models.

Contribution

The paper presents a novel two-stage hybrid approach that uses simple neural networks for feature creation, enhancing model performance and interpretability in credit scoring.

Findings

The hybrid model outperforms the traditional one-stage model in accuracy.

It improves the area under ROC curve and KS statistic.

Neural network-based feature construction captures nonlinear relationships.

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 simple neural network structure as the new feature construction tool in the first stage, then the newly created features are used as the additional input variables in logistic regression in the second stage. The model is compared with the traditional one-stage 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, 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 could overcome the drawbacks of neural networks in terms of its long training time, complex topology, and limited interpretability.