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Response modeling with support vector regression

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dc.contributor.authorKim, Dongil-
dc.contributor.authorLee, Hyoung-joo-
dc.contributor.authorCho, Sungzoon-
dc.date.accessioned2009-08-05T06:23:33Z-
dc.date.available2009-08-05T06:23:33Z-
dc.date.issued2006-12-22-
dc.identifier.citationExpert Systems with Applications, 34(2), 1102 1108en
dc.identifier.issn0957-4174-
dc.identifier.urihttps://hdl.handle.net/10371/6181-
dc.description.abstractResponse 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.
en
dc.description.sponsorshipThis 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.language.isoen-
dc.publisherElsevieren
dc.subjectResponse modelingen
dc.subjectCustomer relationship managementen
dc.subjectDirect marketingen
dc.subjectSupport vector machinesen
dc.subjectRegressionen
dc.subjectPattern selectionen
dc.titleResponse modeling with support vector regressionen
dc.typeArticleen
dc.contributor.AlternativeAuthor김동일-
dc.contributor.AlternativeAuthor이형주-
dc.contributor.AlternativeAuthor조성준-
dc.identifier.doi10.1016/j.eswa.2006.12.019-
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