Ranking Potential Customers Based on Group-Ensemble

Ranking Potential Customers Based on Group-Ensemble

Zhang Zhi-Zhuo (South China University of Technology, China), Chen Qiong (South China University of Technology, China), Ke Shang-Fu (South China University of Technology, China), Wu Yi-Jun (South China University of Technology, China) and Qi Fei (South China University of Technology, China)
DOI: 10.4018/978-1-60566-717-1.ch023
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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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