Machine Learning
Shape quantization and recognition with randomized trees
Neural Computation
Intensification and diversification with elite tabu search solutions for the linear ordering problem
Computers and Operations Research
Rank aggregation methods for the Web
Proceedings of the 10th international conference on World Wide Web
Machine Learning
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Introduction to Stochastic Search and Optimization
Introduction to Stochastic Search and Optimization
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
Machine Learning
Generalization Bounds for the Area Under the ROC Curve
The Journal of Machine Learning Research
SIAM Journal on Discrete Mathematics
Joining ranked inputs in practice
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
The Journal of Machine Learning Research
Label ranking by learning pairwise preferences
Artificial Intelligence
Consistency of Random Forests and Other Averaging Classifiers
The Journal of Machine Learning Research
Computing distances between partial rankings
Information Processing Letters
Journal of Artificial Intelligence Research
Unsupervised rank aggregation with domain-specific expertise
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
IEEE Transactions on Information Theory
ICMLA '09 Proceedings of the 2009 International Conference on Machine Learning and Applications
Adaptive partitioning schemes for bipartite ranking
Machine Learning
Ranking and scoring using empirical risk minimization
COLT'05 Proceedings of the 18th annual conference on Learning Theory
| Hi-index | 0.00 |
The present paper examines how the aggregation and feature randomization principles underlying the algorithm RANDOM FOREST (Breiman, 2001) can be adapted to bipartite ranking. The approach taken here is based on nonparametric scoring and ROC curve optimization in the sense of the AUC criterion. In this problem, aggregation is used to increase the performance of scoring rules produced by ranking trees, as those developed in Cléemençon and Vayatis (2009c). The present work describes the principles for building median scoring rules based on concepts from rank aggregation. Consistency results are derived for these aggregated scoring rules and an algorithm called RANKING FOREST is presented. Furthermore, various strategies for feature randomization are explored through a series of numerical experiments on artificial data sets.