# Random Forest for Label Ranking

arXiv: https://arxiv.org/abs/1608.07710 · HTML: https://mlsanity.com/paper/1608.07710/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2016.

## Built on by (most cited first, 4 of 4)

- [Multi-label Ranking: Mining Multi-label and Label Ranking Data](https://mlsanity.com/paper/2101.00583/) (2021)
- [Label Ranking through Nonparametric Regression](https://mlsanity.com/paper/2111.02749/) (2021)
- [WildWood: a new Random Forest algorithm](https://mlsanity.com/paper/2109.08010/) (2021)
- [Machine Learning Models for Accurately Predicting Properties of CsPbCl3 Perovskite Quantum Dots](https://mlsanity.com/paper/2406.15515/) (2024)

## Abstract (arXiv)

Label ranking aims to learn a mapping from instances to rankings over a finite number of predefined labels. Random forest is a powerful and one of the most successful general-purpose machine learning algorithms of modern times. In this paper, we present a powerful random forest label ranking method which uses random decision trees to retrieve nearest neighbors. We have developed a novel two-step rank aggregation strategy to effectively aggregate neighboring rankings discovered by the random forest into a final predicted ranking. Compared with existing methods, the new random forest method has many advantages including its intrinsically scalable tree data structure, highly parallel-able computational architecture and much superior performance. We present extensive experimental results to demonstrate that our new method achieves the highly competitive performance compared with state-of-the-art methods for datasets with complete ranking and datasets with only partial ranking information.