This is the official repository for the paper Hyperbolic Random Forests.
Hyperbolic space is becoming a popular choice for representing data due to the hierarchical structure - whether implicit or explicit - of many real-world datasets. Along with it comes a need for algorithms capable of solving fundamental tasks, such as classification, in hyperbolic space. Recently, multiple papers have investigated hyperbolic alternatives to hyperplane-based classifiers, such as logistic regression and SVMs. While effective, these approaches struggle with more complex hierarchical data. We, therefore, propose to generalize the well-known random forests to hyperbolic space. We do this by redefining the notion of a split using horospheres. Since finding the globally optimal split is computationally intractable, we find candidate horospheres through a large-margin classifier. To make hyperbolic random forests work on multi-class data and imbalanced experiments, we furthermore outline a new method for combining classes based on their lowest common ancestor and a class-balanced version of the large-margin loss. Experiments on standard and new benchmarks show that our approach outperforms both conventional random forest algorithms and recent hyperbolic classifiers.
The network embeddings can be downloaded from this repository. We provide the WordNet embeddings and labels for our experiments here.
- We make use of the Hyperbolic Hierarchical Clustering and Hyperbolic Image Embeddings repositories.
If you find our work relevant to your research, please cite:
@article{doorenbos2023hyperbolic,
title={Hyperbolic Random Forests},
author={Doorenbos, Lars and M{\'a}rquez-Neila, Pablo and Sznitman, Raphael and Mettes, Pascal},
journal={arXiv preprint arXiv:2308.13279},
year={2023}
}
The code is published under the MIT License.