Pattern selection for support vector regression based response modeling

Dongil Kim, Sungzoon Cho

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


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.

Original languageEnglish
Pages (from-to)8975-8985
Number of pages11
JournalExpert Systems with Applications
Issue number10
StatePublished - Aug 2012

Bibliographical note

Funding Information:
This work was supported by the Brain Korea 21 program in 2006-2011, Seoul R&BD Program (TR080589M0209722), and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No. 2011-0030814 and No. 400-20110010). This work was also supported by the Engineering Research Institute of SNU.


  • Pattern selection
  • Response modeling
  • Support vector regression
  • Training complexity


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