KDD Cup 2009 @ Budapest: feature partitioning and boosting

    We describe the method used in our final submission to KDD Cup 2009 as well as a selection of promising directions that are generally believed to work well but did not justify our expectations. Our final method consists of a combination of a LogitBoost and an ADTree classifier with a feature selection method that, as shaped by the experiments we have conducted, have turned out to be very different from those described in some well-cited surveys. Some methods that failed include distance, information and dependence measures for feature selection as well as combination of classifiers over a partitioned feature set. As another main lesson learned, alternating decision trees and LogitBoost outperformed most classifiers for most feature subsets of the KDD Cup 2009 data.

    M. Kurucz, D. Siklósi, I. Bíró, P. Csizsek, Z. Fekete, R. Iwatt, T. Kiss, A. Szabó
    JMLR: Workshop and Conference Proceedings