Application of Additive Groves Ensemble
with Multiple Counts Feature Evaluation to KDD Cup’09 Small
Data Set
Daria Sorokina ; JMLR W & CP 7:101-109,
2009.
Abstract
This paper describes a field trial for a
recently developed ensemble called Additive Groves on KDD
Cup’09 competition. Additive Groves were applied to three tasks
provided at the competition using the ”small” data set. On one
of the three tasks, appetency, we achieved the best result
among participants who similarly worked with the small dataset
only. Postcompetition analysis showed that less successfull
result on another task, churn, was partially due to
insufficient preprocessing of nominal attributes. Code for
Additive Groves is publicly available as a part of TreeExtra
package. Another part of this package provides an important
preprocessing technique also used for this competition entry,
feature evaluation through bagging with multiple counts.
