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Ranking Potential Customers Based on Group-Ensemble

Ranking Potential Customers Based on Group-Ensemble

Zhi-Zhuo Zhang, Qiong Chen, Shang-Fu Ke, Yi-Jun Wu, Fei Qi, Ying-Peng Zhang
Copyright: © 2008 |Volume: 4 |Issue: 2 |Pages: 11
ISSN: 1548-3924|EISSN: 1548-3932|ISSN: 1548-3924|EISBN13: 9781615202041|EISSN: 1548-3924|DOI: 10.4018/jdwm.2008040109
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MLA

Zhang, Zhi-Zhuo, et al. "Ranking Potential Customers Based on Group-Ensemble." IJDWM vol.4, no.2 2008: pp.79-89. http://doi.org/10.4018/jdwm.2008040109

APA

Zhang, Z., Chen, Q., Ke, S., Wu, Y., Qi, F., & Zhang, Y. (2008). Ranking Potential Customers Based on Group-Ensemble. International Journal of Data Warehousing and Mining (IJDWM), 4(2), 79-89. http://doi.org/10.4018/jdwm.2008040109

Chicago

Zhang, Zhi-Zhuo, et al. "Ranking Potential Customers Based on Group-Ensemble," International Journal of Data Warehousing and Mining (IJDWM) 4, no.2: 79-89. http://doi.org/10.4018/jdwm.2008040109

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Abstract

Ranking potential customers has become an effective tool for company decision makers to design marketing strategies. The task of PAKDD competition 2007 is a cross-selling problem between credit card and home loan, which can also be treated as a ranking potential customers problem. This article proposes a 3-level ranking model, namely Group-Ensemble, to handle such kinds of problems. In our model, Bagging, RankBoost and Expending Regression Tree are applied to solve crucial data mining problems like data imbalance, missing value and time-variant distribution. The article verifies the model with data provided by PAKDD Competition 2007 and shows that Group-Ensemble can make selling strategy much more efficient.

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