A Combination of Boosting and Bagging for KDD Cup 2009 - Fast Scoring on a Large Database

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Jianjun Xie, Viktoria Rojkova, Siddharth Pal, Stephen Coggeshall ;
Proceedings of KDD-Cup 2009 Competition, PMLR 7:35-43, 2009.

Abstract

We present the ideas and methodologies that we used to address the KDD Cup 2009 challenge on rank-ordering the probability of churn, appetency and up-selling of wireless customers. We choose stochastic gradient boosting tree (TreeNet ®) as our main classifier to handle this large unbalanced dataset. In order to further improve the robustness and accuracy of our results, we bag a series of boosted tree models together as our final submission. Through our exploration we conclude that the most critical factors to achieve our results are effective variable preprocessing and selection, proper imbalanced data handling as well as the combination of bagging and boosting techniques.

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