Advertisement

Joint Cutoff Probabilistic Estimation Using Simulation: A Mailing Campaign Application

  • Antonio Bella
  • Cèsar Ferri
  • José Hernández-Orallo
  • María José Ramírez-Quintana
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4881)

Abstract

Frequently, organisations have to face complex situations where decision making is difficult. In these scenarios, several related decisions must be made at a time, which are also bounded by constraints (e.g. inventory/stock limitations, costs, limited resources, time schedules, etc). In this paper, we present a new method to make a good global decision when we have such a complex environment with several local interwoven data mining models. In these situations, the best local cutoff for each model is not usually the best cutoff in global terms. We use simulation with Petri nets to obtain better cutoffs for the data mining models. We apply our approach to a frequent problem in customer relationship management (CRM), more specifically, a direct-marketing campaign design where several alternative products have to be offered to the same house list of customers and with usual inventory limitations. We experimentally compare two different methods to obtain the cutoff for the models (one based on merging the prospective customer lists and using the local cutoffs, and the other based on simulation), illustrating that methods which use simulation to adjust model cutoff obtain better results than a more classical analytical method.

Keywords

Probabilistic Estimation Customer Relationship Management Data Mining Model Campaign Design Additional Data Structure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Berry, M., Linoff, G.: Mastering Data Mining: The Art and Science of Customer Relationship Management. John Wiley & Sons, Inc., Chichester (1999)Google Scholar
  2. 2.
    Demsar, J.: Statistical comparisons of classifiers over multiple data sets. JMLR 7, 1–30 (2006)MathSciNetGoogle Scholar
  3. 3.
    Fagin, R., Kumar, R., Mahdian, M., Sivakumar, D., Vee, E.: Comparing and aggregating rankings with ties. In: PODS 2004. Proceedings of the 32nd symp. on Principles of database systems, pp. 47–58. ACM Press, New York (2004)CrossRefGoogle Scholar
  4. 4.
    Ferri, C., Flach, P., Hernández, J.: Improving the AUC of Probabilistic Estimation Trees. In: Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds.) ECML 2003. LNCS (LNAI), vol. 2837, pp. 121–132. Springer, Heidelberg (2003)Google Scholar
  5. 5.
    Forman, G.: Counting positives accurately despite inaccurate classification. In: Gama, J., Camacho, R., Brazdil, P.B., Jorge, A.M., Torgo, L. (eds.) ECML 2005. LNCS (LNAI), vol. 3720, pp. 564–575. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  6. 6.
    Murata, T.: Petri nets: Properties, analysis and applications. Proceedings of the IEEE 77(4), 541–580 (1989)CrossRefGoogle Scholar
  7. 7.
    Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Elsevier, Amsterdam (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Antonio Bella
    • 1
  • Cèsar Ferri
    • 1
  • José Hernández-Orallo
    • 1
  • María José Ramírez-Quintana
    • 1
  1. 1.Universitat Politécnica de Valencia, DSIC, ValenciaSpain

Personalised recommendations