Ranking Potential Customers Based on Group-Ensemble

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

International Journal of Data Warehousing and Mining (IJDWM), 2008, vol. 4, issue 2, 79-89

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.

Date: 2008
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