用岛屿协同进化提升决策树抗攻击能力
Cultivating Archipelago of Forests: Evolving Robust Decision Trees through Island Coevolution
- 多岛并行演化决策树与对抗样本，保持多样性
- 在20个数据集上同时提升抗干扰性和准确率
- 适合需要可解释且鲁棒模型的场景
决策树因其简洁和可解释性被广泛应用，但对对抗攻击和数据扰动缺乏鲁棒性。本文提出一种基于岛屿的协同进化算法（ICoEvoRDF），在多个独立岛屿上并行演化决策树和对抗扰动，定期迁移优质决策树以增强解空间探索。该方法利用混合纳什均衡进行集成加权，进一步提升性能。在20个基准数据集上的实验表明，ICoEvoRDF在优化对抗准确率和最小最大遗憾方面均优于现有先进方法。其灵活性支持整合多种已有决策树方法，提供统一框架融合多样化解决方案。本方法为构建稳健且可解释的机器学习模型提供了新方向。
原文摘要 · Abstract (English)
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, demonstrating its superior performance compared to state-of-the-art methods in optimizing both adversarial accuracy and minimax regret. The flexibility of ICoEvoRDF allows for the integration of decision trees from various existing methods, providing a unified framework for combining diverse solutions. Our approach offers a promising direction for developing robust and interpretable machine learning models
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