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Data mining problems and solutions for response modeling in CRM

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84217

Shin,  H
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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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: http://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.