# A Siamese Deep Forest

arXiv: https://arxiv.org/abs/1704.08715 · HTML: https://mlsanity.com/paper/1704.08715/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Apr 2017.

## Built on by (most cited first, 3 of 3)

- [A Survey on Epistemic (Model) Uncertainty in Supervised Learning: Recent Advances and Applications](https://mlsanity.com/paper/2111.01968/) (2021)
- [Interpreting Deep Forest through Feature Contribution and MDI Feature Importance](https://mlsanity.com/paper/2305.00805/) (2023)
- [Enhance and Reuse: A Dual-Mechanism Approach to Boost Deep Forest for Label Distribution Learning](https://mlsanity.com/paper/2602.06353/) (2026)

## Abstract (arXiv)

A Siamese Deep Forest (SDF) is proposed in the paper. It is based on the Deep Forest or gcForest proposed by Zhou and Feng and can be viewed as a gcForest modification. It can be also regarded as an alternative to the well-known Siamese neural networks. The SDF uses a modified training set consisting of concatenated pairs of vectors. Moreover, it defines the class distributions in the deep forest as the weighted sum of the tree class probabilities such that the weights are determined in order to reduce distances between similar pairs and to increase them between dissimilar points. We show that the weights can be obtained by solving a quadratic optimization problem. The SDF aims to prevent overfitting which takes place in neural networks when only limited training data are available. The numerical experiments illustrate the proposed distance metric method.