Response modeling with support vector regression
|dc.identifier.citation||Expert Systems with Applications, 34(2), 1102 1108||en|
|dc.description.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 e-tube based sampling for support vector regression (SVR) which leads to better accuracy than the random sampling method.
|dc.description.sponsorship||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.||en|
|dc.subject||Customer relationship management||en|
|dc.subject||Support vector machines||en|
|dc.title||Response modeling with support vector regression||en|