# Open Set Recognition for Random Forest

arXiv: https://arxiv.org/abs/2408.02684 · HTML: https://mlsanity.com/paper/2408.02684/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 53 references that are in the map.
- First posted on arXiv 1 Aug 2024.

## Builds on (most cited first, 10 of 10)

- [Learning to Compare: Relation Network for Few-Shot Learning](https://mlsanity.com/paper/1711.06025/) (2017)
- [Generalized Out-of-Distribution Detection: A Survey](https://mlsanity.com/paper/2110.11334/) (2021)
- [Tabular Data: Deep Learning is Not All You Need](https://mlsanity.com/paper/2106.03253/) (2021)
- [Exploring the Limits of Out-of-Distribution Detection](https://mlsanity.com/paper/2106.03004/) (2021)
- [Towards Open World Recognition](https://mlsanity.com/paper/1412.5687/) (2014)
- [A Random Forest Guided Tour](https://mlsanity.com/paper/1511.05741/) (2015)
- [A Review of Generalized Zero-Shot Learning Methods](https://mlsanity.com/paper/2011.08641/) (2020)
- [Geometry- and Accuracy-Preserving Random Forest Proximities](https://mlsanity.com/paper/2201.12682/) (2022)
- [Quantifying Outlierness of Funds from their Categories using Supervised Similarity](https://mlsanity.com/paper/2308.06882/) (2023)
- [Open-set Recognition based on the Combination of Deep Learning and Ensemble Method for Detecting Unknown Traffic Scenarios](https://mlsanity.com/paper/2105.07635/) (2021)

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

- [Conformal Inference for Open-Set and Imbalanced Classification](https://mlsanity.com/paper/2510.13037/) (2025)
- [Quantile Regression using Random Forest Proximities](https://mlsanity.com/paper/2408.02355/) (2024)
- [Can an unsupervised clustering algorithm reproduce a categorization system?](https://mlsanity.com/paper/2408.10340/) (2024)
- [Interpretable Model-Aware Counterfactual Explanations for Random Forest](https://mlsanity.com/paper/2510.27397/) (2025)

## Abstract (arXiv)

In many real-world classification or recognition tasks, it is often difficult to collect training examples that exhaust all possible classes due to, for example, incomplete knowledge during training or ever changing regimes. Therefore, samples from unknown/novel classes may be encountered in testing/deployment. In such scenarios, the classifiers should be able to i) perform classification on known classes, and at the same time, ii) identify samples from unknown classes. This is known as open-set recognition. Although random forest has been an extremely successful framework as a general-purpose classification (and regression) method, in practice, it usually operates under the closed-set assumption and is not able to identify samples from new classes when run out of the box. In this work, we propose a novel approach to enabling open-set recognition capability for random forest classifiers by incorporating distance metric learning and distance-based open-set recognition. The proposed method is validated on both synthetic and real-world datasets. The experimental results indicate that the proposed approach outperforms state-of-the-art distance-based open-set recognition methods.