Response models based on bagging neural networks

Identifying customers who are likely to respond to a product offering is an important issue in direct marketing.Response models are typically built from historical purchase data. A popular method of choice, logistic regression, is easy to understand and build, but limited in that the model is linear in parameters. Neural networks are nonlinear and have been found to improve predictive accuracies for a variety of business applications. Neural networks have not always demonstrated clear supremacy over traditional statistics competitors, largely because of over-fitting and instability. Combining multiple networksmay alleviate these problems. A systematic method of combining neural networks is proposed, namely bagging or bootstrap aggregating, whereby overfitted multiple neural networks are trained with bootstrap replicas of the original data set and then averaged. We built response models using a publicly available DMEF data set with three methods: bagging neural networks, single neural networks, and conventional logistic regression. The proposed method not only improved but also stabilized the prediction accuracies over the other two.