# One Class Splitting Criteria for Random Forests

arXiv: https://arxiv.org/abs/1611.01971 · HTML: https://mlsanity.com/paper/1611.01971/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2016.

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

- [A Meta-level Analysis of Online Anomaly Detectors](https://mlsanity.com/paper/2209.05899/) (2022)
- [Revisiting randomized choices in isolation forests](https://mlsanity.com/paper/2110.13402/) (2021)
- [Conical Classification For Computationally Efficient One-Class Topic Determination](https://mlsanity.com/paper/2111.00375/) (2021)
- [Isolation forests: looking beyond tree depth](https://mlsanity.com/paper/2111.11639/) (2021)

## Abstract (arXiv)

Random Forests (RFs) are strong machine learning tools for classification and regression. However, they remain supervised algorithms, and no extension of RFs to the one-class setting has been proposed, except for techniques based on second-class sampling. This work fills this gap by proposing a natural methodology to extend standard splitting criteria to the one-class setting, structurally generalizing RFs to one-class classification. An extensive benchmark of seven state-of-the-art anomaly detection algorithms is also presented. This empirically demonstrates the relevance of our approach.