Cultivating Archipelago of Forests: Evolving Robust Decision Trees through Island Coevolution
Adam \.Zychowski, Andrew Perrault, Jacek Ma\'ndziuk

TL;DR
This paper introduces ICoEvoRDF, an island-based coevolutionary algorithm that enhances decision tree ensemble robustness against adversarial attacks by fostering diversity and utilizing game theory for ensemble weighting.
Contribution
It presents a novel coevolutionary framework with island migration and Nash equilibrium-based ensemble weighting to improve decision tree robustness and accuracy.
Findings
ICoEvoRDF outperforms state-of-the-art methods on 20 datasets.
The approach improves adversarial accuracy and minimax regret.
Flexible framework integrates diverse decision tree methods.
Abstract
Decision trees are widely used in machine learning due to their simplicity and interpretability, but they often lack robustness to adversarial attacks and data perturbations. The paper proposes a novel island-based coevolutionary algorithm (ICoEvoRDF) for constructing robust decision tree ensembles. The algorithm operates on multiple islands, each containing populations of decision trees and adversarial perturbations. The populations on each island evolve independently, with periodic migration of top-performing decision trees between islands. This approach fosters diversity and enhances the exploration of the solution space, leading to more robust and accurate decision tree ensembles. ICoEvoRDF utilizes a popular game theory concept of mixed Nash equilibrium for ensemble weighting, which further leads to improvement in results. ICoEvoRDF is evaluated on 20 benchmark datasets,…
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Taxonomy
TopicsIsland Studies and Pacific Affairs · Maritime Ports and Logistics
