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 |Pages: 11
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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