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: |
PDF
|
|
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}
}
|
|
