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Predicting Future Customers via Ensembling Gradually Expanded Trees

Author

Listed:
  • Yang Yu

    (National Laboratory for Novel Software Technology, China)

  • De-Chuan Zhan

    (National Laboratory for Novel Software Technology, China)

  • Xu-Ying Liu

    (National Laboratory for Novel Software Technology, China)

  • Ming Li

    (National Laboratory for Novel Software Technology, China)

  • Zhi-Hua Zhou

    (National Laboratory for Novel Software Technology, China)

Abstract

Our LAMDAer team has won the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2006 Data Mining Competition (open category) grand champion. This report presents our solution to the PAKDD 2006 Data Mining Competition. Following a brief description of the task, we discuss the difficulties of the task and explain the motivation of our solution. Then, we propose the Gradually Expanded Tree Ensemble (GetEnsemble) 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 how to obtain cues on which kind of second generation (2G) customers are likely to become third generation (3G) users with the proposed method.

Suggested Citation

  • Yang Yu & De-Chuan Zhan & Xu-Ying Liu & Ming Li & Zhi-Hua Zhou, 2007. "Predicting Future Customers via Ensembling Gradually Expanded Trees," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 3(2), pages 12-21, April.
    • Handle: RePEc:igg:jdwm00:v:3:y:2007:i:2:p:12-21
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