KDD Cup 2009 @ Budapest: feature partitioning and boosting

Miklós Kurucz, Dávid Siklósi, István Bíró, Péter Csizsek, Zsolt Fekete, Róbert Iwatt, Tamás Kiss and Adrienn Szabó ; JMLR W & CP 7:65-75, 2009.

Abstract

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.



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