# Comparison-Based Random Forests

arXiv: https://arxiv.org/abs/1806.06616 · HTML: https://mlsanity.com/paper/1806.06616/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2018.

## Built on by (most cited first, 2 of 2)

- [Learning-Augmented Priority Queues](https://mlsanity.com/paper/2406.04793/) (2024)
- [Nonparametric Feature Selection by Random Forests and Deep Neural Networks](https://mlsanity.com/paper/2201.06821/) (2022)

## Abstract (arXiv)

Assume we are given a set of items from a general metric space, but we neither have access to the representation of the data nor to the distances between data points. Instead, suppose that we can actively choose a triplet of items (A,B,C) and ask an oracle whether item A is closer to item B or to item C. In this paper, we propose a novel random forest algorithm for regression and classification that relies only on such triplet comparisons. In the theory part of this paper, we establish sufficient conditions for the consistency of such a forest. In a set of comprehensive experiments, we then demonstrate that the proposed random forest is efficient both for classification and regression. In particular, it is even competitive with other methods that have direct access to the metric representation of the data.