# Learn on Source, Refine on Target:A Model Transfer Learning Framework with Random Forests

arXiv: https://arxiv.org/abs/1511.01258 · HTML: https://mlsanity.com/paper/1511.01258/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Nov 2015.

## Built on by (most cited first, 6 of 6)

- [ADAPT : Awesome Domain Adaptation Python Toolbox](https://mlsanity.com/paper/2107.03049/) (2021)
- [Transfer Learning on Electromyography (EMG) Tasks: Approaches and Beyond](https://mlsanity.com/paper/2210.06295/) (2022)
- [Meta-forests: Domain generalization on random forests with meta-learning](https://mlsanity.com/paper/2401.04425/) (2024)
- [Transfer Learning of Surrogate Models via Domain Affine Transformation Across Synthetic and Real-World Benchmarks](https://mlsanity.com/paper/2501.14012/) (2025)
- [Trust in Motion: Capturing Trust Ascendancy in Open-Source Projects using Hybrid AI](https://mlsanity.com/paper/2210.02656/) (2022)
- [Transfer Learning with Distance Covariance for Random Forest: Error Bounds and an EHR Application](https://mlsanity.com/paper/2510.10870/) (2025)

## Abstract (arXiv)

We propose novel model transfer-learning methods that refine a decision forest model M learned within a "source" domain using a training set sampled from a "target" domain, assumed to be a variation of the source. We present two random forest transfer algorithms. The first algorithm searches greedily for locally optimal modifications of each tree structure by trying to locally expand or reduce the tree around individual nodes. The second algorithm does not modify structure, but only the parameter (thresholds) associated with decision nodes. We also propose to combine both methods by considering an ensemble that contains the union of the two forests. The proposed methods exhibit impressive experimental results over a range of problems.