TY - JOUR
T1 - Response modeling with support vector regression
AU - Kim, Dongil
AU - Lee, Hyoung joo
AU - Cho, Sungzoon
N1 - 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.
PY - 2008/2
Y1 - 2008/2
N2 - 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.
AB - 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.
KW - Customer relationship management
KW - Direct marketing
KW - Pattern selection
KW - Regression
KW - Response modeling
KW - Support vector machines
UR - http://www.scopus.com/inward/record.url?scp=36148999852&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2006.12.019
DO - 10.1016/j.eswa.2006.12.019
M3 - Article
AN - SCOPUS:36148999852
SN - 0957-4174
VL - 34
SP - 1102
EP - 1108
JO - Expert Systems with Applications
JF - Expert Systems with Applications
IS - 2
ER -