Data mining problems and solutions for response modeling in CRM
2006
Article
ei
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.
Author(s): | Cho, S. and Shin, H. and Yu, E. and Ha, K. and MacLachlan, D. |
Journal: | Entrue Journal of Information Technology |
Volume: | 5 |
Number (issue): | 1 |
Pages: | 55-64 |
Year: | 2006 |
Month: | March |
Day: | 0 |
Department(s): | Empirical Inference |
Bibtex Type: | Article (article) |
Digital: | 0 |
Language: | en |
Organization: | Max-Planck-Gesellschaft |
School: | Biologische Kybernetik |
Links: |
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BibTex @article{3816, title = {Data mining problems and solutions for response modeling in CRM}, author = {Cho, S. and Shin, H. and Yu, E. and Ha, K. and MacLachlan, D.}, journal = {Entrue Journal of Information Technology}, volume = {5}, number = {1}, pages = {55-64}, organization = {Max-Planck-Gesellschaft}, school = {Biologische Kybernetik}, month = mar, year = {2006}, doi = {}, month_numeric = {3} } |