Application of Additive Groves Ensemble with Multiple Counts Feature Evaluation to KDD Cup'09 Small Data Set

Daria Sorokina
Proceedings of KDD-Cup 2009 Competition, PMLR 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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v7-sorokina09, title = {Application of Additive Groves Ensemble with Multiple Counts Feature Evaluation to KDD Cup'09 Small Data Set}, author = {Sorokina, Daria}, booktitle = {Proceedings of KDD-Cup 2009 Competition}, pages = {101--109}, year = {2009}, editor = {Dror, Gideon and Boullé, Mar and Guyon, Isabelle and Lemaire, Vincent and Vogel, David}, volume = {7}, series = {Proceedings of Machine Learning Research}, address = {New York, New York, USA}, month = {28 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v7/sorokina09/sorokina09.pdf}, url = {https://proceedings.mlr.press/v7/sorokina09.html}, 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.} }
Endnote
%0 Conference Paper %T Application of Additive Groves Ensemble with Multiple Counts Feature Evaluation to KDD Cup'09 Small Data Set %A Daria Sorokina %B Proceedings of KDD-Cup 2009 Competition %C Proceedings of Machine Learning Research %D 2009 %E Gideon Dror %E Mar Boullé %E Isabelle Guyon %E Vincent Lemaire %E David Vogel %F pmlr-v7-sorokina09 %I PMLR %P 101--109 %U https://proceedings.mlr.press/v7/sorokina09.html %V 7 %X 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.
RIS
TY - CPAPER TI - Application of Additive Groves Ensemble with Multiple Counts Feature Evaluation to KDD Cup'09 Small Data Set AU - Daria Sorokina BT - Proceedings of KDD-Cup 2009 Competition DA - 2009/12/04 ED - Gideon Dror ED - Mar Boullé ED - Isabelle Guyon ED - Vincent Lemaire ED - David Vogel ID - pmlr-v7-sorokina09 PB - PMLR DP - Proceedings of Machine Learning Research VL - 7 SP - 101 EP - 109 L1 - http://proceedings.mlr.press/v7/sorokina09/sorokina09.pdf UR - https://proceedings.mlr.press/v7/sorokina09.html AB - 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. ER -
APA
Sorokina, D.. (2009). Application of Additive Groves Ensemble with Multiple Counts Feature Evaluation to KDD Cup'09 Small Data Set. Proceedings of KDD-Cup 2009 Competition, in Proceedings of Machine Learning Research 7:101-109 Available from https://proceedings.mlr.press/v7/sorokina09.html.

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