A Classifier Ensemble Model Based on GMDH-Type Neural Network for Customer Targeting

  • Jin Xiao
  • Changzheng He
  • Shouyang Wang
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 241)


With the speedy development of information technology, database marketing has become a hot topic for both marketing practitioners and scholars, and customer targeting modeling is a top priority in database marketing. To overcome the limitations of the existing models, this study proposes a classifier ensemble model based on group method of data handling (GMDH) type neural network for customer targeting. It first utilizes GMDH-type neural network to select the key explanatory variables, trains a series of base classification models, and then conducts the classifier ensemble selection based on the forecasting results of the base models by GMDH-type neural network again to get the final ensemble model. The empirical analysis results show that the hit rate of the proposed model is better than that of some existing models, and it can bring more profits for the enterprise.


Customer targeting model GMDH-type neural network Multipleclassifiers ensemble Database marketing 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Blattberg RC, Kim, BD, Neslin SA (2008) Database marketing: Analyzing and managing customers. SpringerGoogle Scholar
  2. 2.
    Ha K, Cho, S, MacLachlan D (2005) Response models based on bagging neural networks. Journal of Interactive Marketing 19:17–30Google Scholar
  3. 3.
    Bewick V, Cheek L, Ball, J (2005) Statistics review 14: logistic regression. Crit Care 9:112–118Google Scholar
  4. 4.
    Kim YS, Street WN, Russell GJ et al (2005) Customer targeting: A neural network approach guided by genetic algorithms. Management Science 51:264–276Google Scholar
  5. 5.
    Hansen LK, Salamon P (1990) Neural network ensembles. IEEE Transactions on Pattern Analysis and Machine Intelligence 12:993–1001Google Scholar
  6. 6.
    Johnson RA, Wichern DW (1992) Applied multivariate statistical analysis. 3rd ed. Prentice Hall, Englewood Cliffs, NJGoogle Scholar
  7. 7.
    Zhou, ZH, Wu J, Tang W (2002) Ensembling neural networks: Many could be better than all. Artificial Intelligence 137:239–263Google Scholar
  8. 8.
    Ivakhnenko AG (1976) The group method of data handling in prediction problems. Soviet Automatic Control 9:21–30Google Scholar
  9. 9.
    Sarychev AP (1990) An averaged regularity criterion for the group method of data handling in the problem of searching for the best regression. Soviet Journal of Automation and Information Sciences cc of Avtomatika 23:24–29Google Scholar
  10. 10.
    Abdel-Aal RE, Elhadidy MA, Shaahid SM (2008) Modeling and forecasting the mean hourly wind speed time series using GMDH-based abductive networks. Renewable Energy 34:1686–1699Google Scholar
  11. 11.
    He CZ (2005) Self-organising data mining and economic forecasting. Science Publish (In Chinese)Google Scholar
  12. 12.
    Mehrara M, Moeini A, Ahrari M et al (2009) Investigating the efficiency in oil futures market based on GMDH approach. Expert Systems with Applications 36:7479–7483Google Scholar
  13. 13.
    Puig V,Witczak M, Nejjari F et al (2007) A GMDH neural network-based approach to passive robust fault detection using a constraint satisfaction backward test. Engineering Applications of Artificial Intelligence 20:886–897Google Scholar
  14. 14.
    Muller JA, Lemke F (2000) Self-organising data mining: An intelligent approach to extract knowledge from data. Dresden, BerlinGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.School of Political Science and Public AdministrationUniversity of Electronic Science and Technology of ChinaChengduPeople’s Republic of China

Personalised recommendations