Data mining problems and solutions for response modeling in CRM

Cho, S; Shin, H; Yu , E; Ha, K; MacLachlan, D

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Data mining problems and solutions for response modeling in CRM

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Shin, H
Friedrich Miescher Laboratory, Max Planck Society;

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https://pdfs.semanticscholar.org/690f/4c00fd80e1716af80f9a688a67927deb9efc.pdf
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Zitation

Cho, S., Shin, H., Yu, E., Ha, K., & MacLachlan, D. (2006). Data mining problems and solutions for response modeling in CRM. Entrue Journal of Information Technology, 5(1), 55-64.

Zitierlink: https://hdl.handle.net/11858/00-001M-0000-0013-D25D-A

Zusammenfassung

We present three data mining problems that are often encountered in building a response model. They are robust modeling, variable selection and data selection. Respective algorithmic solutions are given. They are bagging based ensemble, genetic algorithm based wrapper approach and nearest neighbor-based data selection in that order. A real world data set from Direct Marketing Educational Foundation, or DMEF4, is used to show their effectiveness. Proposed methods were found to solve the problems in a practical way.