用机器学习修复损坏的代数结构，提升修复成功率。
A Hybrid Framework for Healing Semigroups with Machine Learning
- 结合确定性策略与随机森林分类器进行修复
- 在15%损坏率下，六阶半群修复率达95%，十阶达60%
- 适合代数计算、形式化验证等需要结构完整性的领域
本文提出一种混合框架，用于修复受损的有限半群，结合确定性修复策略与基于随机森林分类器的机器学习方法。表格中的损坏会破坏结合律，使代数结构失效。确定性方法在小规模（n≤6）和低损坏率下有效，但随规模和损坏率上升迅速退化。我们在Mace4生成的数据集上进行了实验，结果表明，该混合框架在修复率上优于仅用确定性或仅用机器学习的方法。在损坏率p=15%时，对最大基数n=6的半群修复率达95%，n=10时修复率为60%。
原文摘要 · Abstract (English)
In this paper, we propose a hybrid framework that heals corrupted finite semigroups, combining deterministic repair strategies with Machine Learning using a Random Forest Classifier. Corruption in these tables breaks associativity and invalidates the algebraic structure. Deterministic methods work for small cardinality n and low corruption but degrade rapidly. Our experiments, carried out on Mace4-generated data sets, demonstrate that our hybrid framework achieves higher healing rates than deterministic-only and ML-only baselines. At a corruption percentage of p=15%, our framework healed 95% of semigroups up to cardinality n=6 and 60% at n=10.
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