Title of the poster: Logistic Model Trees with AUCsplit Criterion for the KDD Cup 2009 Small Challenge
Abstract for the poster:

This poster shows an overview of the approaches to the "Small Challenge" of the KDD cup 2009. Our most successful method was a Logistic Model Tree with AUC as split criterion using predictions from boosted decision stumps as features. This was the best submission for the "Small Challenge" that did not use additional data from other feature sets. The second approach was to use an AUC-optimized weighted linear combination of several rankings. The poster presents the key ideas of preprocessing steps and contains some main experimental results.


Title of the slides: Combining Boosting with Trees for the KDD Cup 2009
Abstract of the slides:

We describe our approach to the "Small Challenge" of the KDD cup 2009.  From the given features, we extracted additional binary features and imputed missing values using SVMs and Decision Trees. Our most successful method was a Logistic Model Tree with AUC as split criterion using predictions from boosted decision stumps as features. This was the best submission for the "Small Challenge'' that did not use additional data from other feature sets. A second approach using an AUC-optimized weighted linear combination of several rankings scored only slightly worse.