Response modeling with support vector regression

Dongil Kim, Hyoung joo Lee, Sungzoon Cho

Research output: Contribution to journalArticlepeer-review

32 Scopus citations

Abstract

Response modeling has become a key factor to direct marketing. In general, there are two stages in response modeling. The first stage is to identify respondents from a customer database while the second stage is to estimate purchase amounts of the respondents. This paper focuses on the second stage where a regression, not a classification, problem is solved. Recently, several non-linear models based on machine learning such as support vector machines (SVM) have been applied to response modeling. However, there is a major difficulty. A typical training dataset for response modeling is so large that modeling takes very long, or, even worse, modeling may be impossible. Therefore, sampling methods have been usually employed in practice. However a sampled dataset usually leads to lower accuracy. In this paper, we employed an ε-tube based sampling for support vector regression (SVR) which leads to better accuracy than the random sampling method.

Original languageEnglish
Pages (from-to)1102-1108
Number of pages7
JournalExpert Systems with Applications
Volume34
Issue number2
DOIs
StatePublished - Feb 2008

Bibliographical note

Funding Information:
This work was partially supported by Grant No. R01-2005-000-103900-0 from Basic Research Program of the Korea Science and Engineering Foundation, Brain Korea 21, and Engineering Research Institute of SNU.

Keywords

  • Customer relationship management
  • Direct marketing
  • Pattern selection
  • Regression
  • Response modeling
  • Support vector machines

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