# Improving Label Ranking Ensembles using Boosting Techniques

arXiv: https://arxiv.org/abs/2001.07744 · HTML: https://mlsanity.com/paper/2001.07744/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jan 2020.

## Built on by (most cited first, 1 of 1)

- [Multimodal Label Relevance Ranking via Reinforcement Learning](https://mlsanity.com/paper/2407.13221/) (2024)

## Abstract (arXiv)

Label ranking is a prediction task which deals with learning a mapping between an instance and a ranking (i.e., order) of labels from a finite set, representing their relevance to the instance. Boosting is a well-known and reliable ensemble technique that was shown to often outperform other learning algorithms. While boosting algorithms were developed for a multitude of machine learning tasks, label ranking tasks were overlooked. In this paper, we propose a boosting algorithm which was specifically designed for label ranking tasks. Extensive evaluation of the proposed algorithm on 24 semi-synthetic and real-world label ranking datasets shows that it significantly outperforms existing state-of-the-art label ranking algorithms.