A Hybrid Framework for Healing Semigroups with Machine Learning
This addresses a specific algebraic structure repair problem for researchers in computational algebra, but it is incremental as it builds on existing deterministic and ML methods.
The paper tackles the problem of healing corrupted finite semigroups by proposing a hybrid framework that combines deterministic repair strategies with a Random Forest Classifier, achieving healing rates of 95% for semigroups up to cardinality n=6 and 60% at n=10 under 15% corruption.
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.