Construction of Bayesian classifiers with GA for response modeling in direct marketing

In this paper, a Bayesian classifier for modeling consumer response to direct marketing is constructed based on a novel genetic algorithm (GA). To evaluate the performance of this model, we test it with a large amount of validation data of direct marketing and compare the results with other benchmark methods, including recency-frequency-monetary (RFM) analysis, Chi-Square automatic interaction detector (CHAID), logistic regression (LR) and so on. The results demonstrate the superiority of this model over the others in terms of accuracy of prediction and interpretable of results. Recently, it has been adopted by a credit card company to effectively handle business problems.