Abstract
We describe, analyze and experiment with a boosting algorithm for multilabel categorization problems. Our algorithm includes as special cases previously studied boosting algorithms such as Adaboost.MH. We cast the multilabel problem as multiple binary decision problems, based on a user-defined covering of the set of labels. We prove a lower bound on the progress made by our algorithm on each boosting iteration and demonstrate the merits of our algorithm in experiments with text categorization problems.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 27-34 |
| Number of pages | 8 |
| Journal | Journal of Machine Learning Research |
| Volume | 2 |
| State | Published - 2007 |
| Event | 11th International Conference on Artificial Intelligence and Statistics, AISTATS 2007 - San Juan, Puerto Rico Duration: Mar 21 2007 → Mar 24 2007 |
All Science Journal Classification (ASJC) codes
- Software
- Control and Systems Engineering
- Statistics and Probability
- Artificial Intelligence