Learning to rank with extremely randomized trees


Pierre Geurts, Gilles Louppe ;
Proceedings of the Learning to Rank Challenge, PMLR 14:49-61, 2011.


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.

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