Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Blattberg RC, Kim, BD, Neslin SA (2008) Database marketing: Analyzing and managing customers. Springer
Ha K, Cho, S, MacLachlan D (2005) Response models based on bagging neural networks. Journal of Interactive Marketing 19:17–30
Bewick V, Cheek L, Ball, J (2005) Statistics review 14: logistic regression. Crit Care 9:112–118
Kim YS, Street WN, Russell GJ et al (2005) Customer targeting: A neural network approach guided by genetic algorithms. Management Science 51:264–276
Hansen LK, Salamon P (1990) Neural network ensembles. IEEE Transactions on Pattern Analysis and Machine Intelligence 12:993–1001
Johnson RA, Wichern DW (1992) Applied multivariate statistical analysis. 3rd ed. Prentice Hall, Englewood Cliffs, NJ
Zhou, ZH, Wu J, Tang W (2002) Ensembling neural networks: Many could be better than all. Artificial Intelligence 137:239–263
Ivakhnenko AG (1976) The group method of data handling in prediction problems. Soviet Automatic Control 9:21–30
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 23:24–29
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 34:1686–1699
He CZ (2005) Self-organising data mining and economic forecasting. Science Publish (In Chinese)
Mehrara M, Moeini A, Ahrari M et al (2009) Investigating the efficiency in oil futures market based on GMDH approach. Expert Systems with Applications 36:7479–7483
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 20:886–897
Muller JA, Lemke F (2000) Self-organising data mining: An intelligent approach to extract knowledge from data. Dresden, Berlin
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Xiao, J., He, C., Wang, S. (2014). A Classifier Ensemble Model Based on GMDH-Type Neural Network for Customer Targeting. In: Xu, J., Fry, J., Lev, B., Hajiyev, A. (eds) Proceedings of the Seventh International Conference on Management Science and Engineering Management. Lecture Notes in Electrical Engineering, vol 241. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40078-0_22
Download citation
DOI: https://doi.org/10.1007/978-3-642-40078-0_22
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40077-3
Online ISBN: 978-3-642-40078-0
eBook Packages: Business and EconomicsBusiness and Management (R0)