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Learning to rank with extremely randomized trees
Geurts, Pierre; Louppe, Gilles
2011In Proceedings of Machine Learning Research, 14, p. 49-61
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Keywords :
learning to rank; regression trees; ensemble methods; transfer learning
Abstract :
[en] In this paper, we report on our experiments on the Yahoo! Labs Learning to Rank challenge organized in the context of the 23rd International Conference of Machine Learning (ICML 2010). We competed in both the learning to rank and the transfer learning tracks of the challenge with several tree-based ensemble methods, including Tree Bagging, Random Forests, and Extremely Randomized Trees. Our methods ranked 10th in the first track and 4th in the second track. Although not at the very top of the ranking, our results show that ensembles of randomized trees are quite competitive for the “learning to rank” problem. The paper also analyzes computing times of our algorithms and presents some post-challenge experiments with transfer learning methods.
Disciplines :
Computer science
Author, co-author :
Geurts, Pierre ;  Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Louppe, Gilles  ;  Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation
Language :
English
Title :
Learning to rank with extremely randomized trees
Publication date :
January 2011
Event name :
ICML Workshop - Yahoo! Learning to Rank Challenge
Event place :
Haifa, Israel
Event date :
25 juin 2010
By request :
Yes
Audience :
International
Journal title :
Proceedings of Machine Learning Research
eISSN :
2640-3498
Publisher :
Microtome Publishing, Brookline, United States - Massachusetts
Special issue title :
Yahoo! Learning to Rank Challenge
Volume :
14
Pages :
49-61
Peer reviewed :
Peer Reviewed verified by ORBi
Funders :
F.R.S.-FNRS - Fonds de la Recherche Scientifique [BE]
Available on ORBi :
since 11 February 2011

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