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Response modeling with support vector regression

Cited 20 time in Web of Science Cited 23 time in Scopus
Authors
Kim, Dongil; Lee, Hyoung-joo; Cho, Sungzoon
Issue Date
2006-12-22
Publisher
Elsevier
Citation
Expert Systems with Applications, 34(2), 1102 1108
Keywords
Response modelingCustomer relationship managementDirect marketingSupport vector machinesRegressionPattern selection
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.
ISSN
0957-4174
Language
English
URI
https://hdl.handle.net/10371/6181
DOI
https://doi.org/10.1016/j.eswa.2006.12.019
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