# Genetic Adversarial Training of Decision Trees

arXiv: https://arxiv.org/abs/2012.11352 · HTML: https://mlsanity.com/paper/2012.11352/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Dec 2020.

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

- [Fair Training of Decision Tree Classifiers](https://mlsanity.com/paper/2101.00909/) (2021)
- [Beyond Robustness: Resilience Verification of Tree-Based Classifiers](https://mlsanity.com/paper/2112.02705/) (2021)
- [Verifiable Learning for Robust Tree Ensembles](https://mlsanity.com/paper/2305.03626/) (2023)
- [Coevolutionary Algorithm for Building Robust Decision Trees under Minimax Regret](https://mlsanity.com/paper/2312.09078/) (2023)
- [Adversarial Example Detection in Deployed Tree Ensembles](https://mlsanity.com/paper/2206.13083/) (2022)
- [Verifiable Boosted Tree Ensembles](https://mlsanity.com/paper/2402.14988/) (2024)

## Abstract (arXiv)

We put forward a novel learning methodology for ensembles of decision trees based on a genetic algorithm which is able to train a decision tree for maximizing both its accuracy and its robustness to adversarial perturbations. This learning algorithm internally leverages a complete formal verification technique for robustness properties of decision trees based on abstract interpretation, a well known static program analysis technique. We implemented this genetic adversarial training algorithm in a tool called Meta-Silvae (MS) and we experimentally evaluated it on some reference datasets used in adversarial training. The experimental results show that MS is able to train robust models that compete with and often improve on the current state-of-the-art of adversarial training of decision trees while being much more compact and therefore interpretable and efficient tree models.