Predicting Future Customers via Ensembling Gradually Expanded Trees

Our LAMDAer team has won the PAKDD'06 Data Mining Competition (Open Category) Grand Champion. This report presents our solution to PAKDD'06 Data Mining Competition. Following a brief description on the task, we discuss the difficulties of the task and explain the motivation of our solution. Then, we propose the GetEnsemble (Gradually Expanded Tree Ensemble) method, which handles the difficulties via ensembling expanded trees. We evaluated the proposed method and several other methods using AUC, and found the proposed method beats others in this task. Besides, we show that how to obtain some cues on which kind of 2G customers are likely to become 3G users with the proposed method. Key words: Ensemble Learning, Cost Sensitive Learning, Class Imbalance Learning, Semi-supervised Learning, Distribution Expanding.

• Type: Journal
• Year: 2007
• Where: JDWM
• Authors: Yang Yu, De-Chuan Zhan, Xu-Ying Liu, Ming Li, Zhi-Hua Zhou
• Keywords: Data Mining Competition, Grand Champion, JDWM 2007, LAMDAer Team