提出高效机器遗忘框架DynFrs，实现随机森林快速删数且不损失准确率。
DynFrs: An Efficient Framework for Machine Unlearning in Random Forest
- 用概率采样限制样本参与树的数量，降低删除影响。
- 延迟重构节点，避免不必要的结构修改，提速数个量级。
- 适配各类随机森林模型，适合隐私敏感场景应用。
随机森林在医疗诊断、金融风控和个性化推荐等敏感领域广泛应用，但面临用户数据删除权（如GDPR、CCPA）的挑战。现有机器遗忘方法效率低且难落地。本文提出DynFrs框架，结合子采样方法Occ(q)与懒惰标记策略Lzy，使每个训练样本仅出现在部分树中，从而限制删除影响；同时延迟树节点重建，减少冗余操作。实验表明，在极化随机树（Extremely Randomized Trees）上，DynFrs实现数量级加速，相比现有方法显著提升遗忘速度与预测精度。
原文摘要 · Abstract (English)
Random Forests are widely recognized for establishing efficacy in classification and regression tasks, standing out in various domains such as medical diagnosis, finance, and personalized recommendations. These domains, however, are inherently sensitive to privacy concerns, as personal and confidential data are involved. With increasing demand for the right to be forgotten, particularly under regulations such as GDPR and CCPA, the ability to perform machine unlearning has become crucial for Random Forests. However, insufficient attention was paid to this topic, and existing approaches face difficulties in being applied to real-world scenarios. Addressing this gap, we propose the DynFrs framework designed to enable efficient machine unlearning in Random Forests while preserving predictive accuracy. Dynfrs leverages subsampling method Occ(q) and a lazy tag strategy Lzy, and is still adaptable to any Random Forest variant. In essence, Occ(q) ensures that each sample in the training set occurs only in a proportion of trees so that the impact of deleting samples is limited, and Lzy delays the reconstruction of a tree node until necessary, thereby avoiding unnecessary modifications on tree structures. In experiments, applying Dynfrs on Extremely Randomized Trees yields substantial improvements, achieving orders of magnitude faster unlearning performance and better predictive accuracy than existing machine unlearning methods for Random Forests.
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