# Random Similarity Forests

arXiv: https://arxiv.org/abs/2204.05389 · HTML: https://mlsanity.com/paper/2204.05389/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 18 references that are in the map.
- First posted on arXiv 11 Apr 2022.

## Builds on (most cited first, 1 of 1)

- [netrd: A library for network reconstruction and graph distances](https://mlsanity.com/paper/2010.16019/) (2020)

## Built on by (most cited first, 1 of 1)

- [Random Similarity Isolation Forests](https://mlsanity.com/paper/2502.19122/) (2025)

## Abstract (arXiv)

The wealth of data being gathered about humans and their surroundings drives new machine learning applications in various fields. Consequently, more and more often, classifiers are trained using not only numerical data but also complex data objects. For example, multi-omics analyses attempt to combine numerical descriptions with distributions, time series data, discrete sequences, and graphs. Such integration of data from different domains requires either omitting some of the data, creating separate models for different formats, or simplifying some of the data to adhere to a shared scale and format, all of which can hinder predictive performance. In this paper, we propose a classification method capable of handling datasets with features of arbitrary data types while retaining each feature's characteristic. The proposed algorithm, called Random Similarity Forest, uses multiple domain-specific distance measures to combine the predictive performance of Random Forests with the flexibility of Similarity Forests. We show that Random Similarity Forests are on par with Random Forests on numerical data and outperform them on datasets from complex or mixed data domains. Our results highlight the applicability of Random Similarity Forests to noisy, multi-source datasets that are becoming ubiquitous in high-impact life science projects.