# Online Boosting Algorithms for Multi-label Ranking

arXiv: https://arxiv.org/abs/1710.08079 · HTML: https://mlsanity.com/paper/1710.08079/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2017.

## Built on by (most cited first, 8 of 8)

- [A Boosting Approach to Reinforcement Learning](https://mlsanity.com/paper/2108.09767/) (2021)
- [Boosting for Online Convex Optimization](https://mlsanity.com/paper/2102.09305/) (2021)
- [On the Learnability of Multilabel Ranking](https://mlsanity.com/paper/2304.03337/) (2023)
- [Gradient-based Label Binning in Multi-label Classification](https://mlsanity.com/paper/2106.11690/) (2021)
- [Skeptical inferences in multi-label ranking with sets of probabilities](https://mlsanity.com/paper/2210.08576/) (2022)
- [GaussianMLR: Learning Implicit Class Significance via Calibrated Multi-Label Ranking](https://mlsanity.com/paper/2303.03907/) (2023)
- [Game Theory Solutions in Sensor-Based Human Activity Recognition: A Review](https://mlsanity.com/paper/2311.06311/) (2023)
- [UniMLR: Modeling Implicit Class Significance for Multi-Label Ranking](https://mlsanity.com/paper/2508.21772/) (2025)

## Abstract (arXiv)

We consider the multi-label ranking approach to multi-label learning. Boosting is a natural method for multi-label ranking as it aggregates weak predictions through majority votes, which can be directly used as scores to produce a ranking of the labels. We design online boosting algorithms with provable loss bounds for multi-label ranking. We show that our first algorithm is optimal in terms of the number of learners required to attain a desired accuracy, but it requires knowledge of the edge of the weak learners. We also design an adaptive algorithm that does not require this knowledge and is hence more practical. Experimental results on real data sets demonstrate that our algorithms are at least as good as existing batch boosting algorithms.