Pattern selection for support vector regression based response modeling

Two-stage response modeling, identifying respondents and then ranking them according to their expected profit, was proposed in order to increase the profit of direct marketing. For the second stage of two-stage response modeling, support vector regression (SVR) has been successfully employed due to its great generalization performances. However, the training complexities of SVR have made it difficult to apply to response modeling based on the large amount of data. In this paper, we propose a pattern selection method called Expected Margin based Pattern Selection (EMPS) to reduce the training complexities of SVR for use as a response modeling dataset with high dimensionality and high nonlinearity. EMPS estimates the expected margin for all training patterns and selects patterns which are likely to become support vectors. The experimental results involving 20 benchmark datasets and one real-world marketing dataset showed that EMPS improved SVR efficiency for response modeling.