Logistic Model Trees with AUC Split Criterion for the KDD Cup 2009 Small Challenge

In this work, we describe our approach to the "Small Challenge" of the KDD cup 2009, a classification task with incomplete data. Preprocessing, feature extraction and model selection are documented in detail. We suggest a criterion based on the number of missing values to select a suitable imputation method for each feature. Logistic Model Trees (LMT) are extended with a split criterion optimizing the Area under the ROC Curve (AUC), which was the requested evaluation criterion. By stacking boosted decision stumps and LMT we achieved the best result for the "Small Challenge" without making use of additional data from other feature sets, resulting in an AUC score of 0.8081. We also present results of an AUC optimizing model combination that scored only slightly worse with an AUC score of 0.8074.