Ranking Potential Customers Based on Group-Ensemble

Zhi-Zhuo Zhang; Qiong Chen; Shang-Fu Ke; Yi-Jun Wu; Fei Qi; Ying-Peng Zhang

doi:10.4018/jdwm.2008040109

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Ranking Potential Customers Based on Group-Ensemble

Zhi-Zhuo Zhang (South China University of Technology, China), Qiong Chen (South China University of Technology, China), Shang-Fu Ke (South China University of Technology, China), Yi-Jun Wu (South China University of Technology, China), Fei Qi (South China University of Technology, China) and Ying-Peng Zhang (South China University of Technology, China)

Source Title: International Journal of Data Warehousing and Mining (IJDWM) 4(2)

DOI: 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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Ranking Potential Customers Based on Group-Ensemble

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