Abstract
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 Clemencon 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.
| Original language | English |
|---|---|
| Pages (from-to) | 39-73 |
| Number of pages | 35 |
| Journal | Journal of Machine Learning Research |
| Volume | 14 |
| Issue number | 1 |
| Publication status | Published - 1 Jan 2013 |
| Externally published | Yes |
Keywords
- AUC criterion
- Bagging
- Bipartite ranking
- Bootstrap
- Classification data
- Feature randomization
- Median ranking
- Nonparametric scoring
- ROC optimization
- Rank aggregation
- Tree-based ranking rules