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.
