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Predicting Future Customers via Ensembling Gradually Expanded Trees

Predicting Future Customers via Ensembling Gradually Expanded Trees

Yang Yu, De-Chuan Zhan, Xu-Ying Liu, Ming Li, Zhi-Hua Zhou
Copyright: © 2007 |Volume: 3 |Issue: 2 |Pages: 10
ISSN: 1548-3924|EISSN: 1548-3932|ISSN: 1548-3924|EISBN13: 9781615202089|EISSN: 1548-3924|DOI: 10.4018/jdwm.2007040102
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MLA

Yu, Yang, et al. "Predicting Future Customers via Ensembling Gradually Expanded Trees." IJDWM vol.3, no.2 2007: pp.12-21. http://doi.org/10.4018/jdwm.2007040102

APA

Yu, Y., Zhan, D., Liu, X., Li, M., & Zhou, Z. (2007). Predicting Future Customers via Ensembling Gradually Expanded Trees. International Journal of Data Warehousing and Mining (IJDWM), 3(2), 12-21. http://doi.org/10.4018/jdwm.2007040102

Chicago

Yu, Yang, et al. "Predicting Future Customers via Ensembling Gradually Expanded Trees," International Journal of Data Warehousing and Mining (IJDWM) 3, no.2: 12-21. http://doi.org/10.4018/jdwm.2007040102

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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.

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