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.

    Csatolmány: 
    Év: 
    2009
    Szerzők: 
    M. Kurucz, D. Siklósi, I. Bíró, P. Csizsek, Z. Fekete, R. Iwatt, T. Kiss, A. Szabó
    Kiadvány: 
    JMLR: Workshop and Conference Proceedings