References of "JMLR: Workshop and Conference Proceedings"
     in
Bookmark and Share    
Full Text
Peer Reviewed
See detailLearning from positive and unlabeled examples by enforcing statistical significance
Geurts, Pierre ULg

in JMLR: Workshop and Conference Proceedings (2011, April), 15

Given a finite but large set of objects de- scribed by a vector of features, only a small subset of which have been labeled as ‘positive’ with respect to a class of interest, we consider the problem of ... [more ▼]

Given a finite but large set of objects de- scribed by a vector of features, only a small subset of which have been labeled as ‘positive’ with respect to a class of interest, we consider the problem of characterizing the positive class. We formalize this as the problem of learning a feature based score function that minimizes the p-value of a non parametric statistical hypothesis test. For lin- ear score functions over the original feature space or over one of its kernelized versions, we provide a solution of this problem computed by a one-class SVM applied on a surrogate dataset obtained by sampling subsets of the overall set of objects and representing them by their average feature-vector shifted by the average feature-vector of the original sample of positive examples. We carry out experiments with this method on the prediction of targets of transcription factors in two different organisms, E. Coli and S. Cererevisiae. Our method extends enrichment analysis commonly carried out in Bioinformatics and its results outperform common solutions to this problem. [less ▲]

Detailed reference viewed: 150 (28 ULg)
Full Text
Peer Reviewed
See detailLearning to rank with extremely randomized trees
Geurts, Pierre ULg; Louppe, Gilles ULg

in JMLR: Workshop and Conference Proceedings (2011, January), 14

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 ... [more ▼]

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. [less ▲]

Detailed reference viewed: 355 (73 ULg)
Full Text
Peer Reviewed
See detailExploiting tree-based variable importances to selectively identify relevant variables
Huynh-Thu, Vân Anh ULg; Wehenkel, Louis ULg; Geurts, Pierre ULg

in JMLR: Workshop and Conference Proceedings (2008), 4

Detailed reference viewed: 94 (36 ULg)