# Crossbreeding in Random Forest

arXiv: https://arxiv.org/abs/2101.08585 · HTML: https://mlsanity.com/paper/2101.08585/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 59 references that are in the map.
- First posted on arXiv 21 Jan 2021.

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

- [Multiple decision trees](https://mlsanity.com/paper/1304.2363/) (2013)

## Abstract (arXiv)

Ensemble learning methods are designed to benefit from multiple learning algorithms for better predictive performance. The tradeoff of this improved performance is slower speed and larger size of ensemble learning systems compared to single learning systems. In this paper, we present a novel approach to deal with this problem in Random Forest (RF) as one of the most powerful ensemble methods. The method is based on crossbreeding of the best tree branches to increase the performance of RF in space and speed while keeping the performance in the classification measures. The proposed approach has been tested on a group of synthetic and real datasets and compared to the standard RF approach. Several evaluations have been conducted to determine the effects of the Crossbred RF (CRF) on the accuracy and the number of trees in a forest. The results show better performance of CRF compared to RF.