Ranking potential customers based on group-ensemble

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

来源：International Journal of Data Warehousing and Mining, 2008, 4(2): 79-89.

DOI：10.4018/jdwm.2008040109

• Bagging
• Group-ensemble
• Rankboost
• Ranking model
• Regression trees

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 loam which can also be treated as a ranking potential customers problem. This articleproposes a 3-level ranking model, namely Group-Ensemble, to handle such kinds of problems. In ourmodel, 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.

• 出版日期2008-4
• 单位华南理工大学