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.
Cite
@article{arxiv.2509.01763,
title = {A Hybrid Framework for Healing Semigroups with Machine Learning},
author = {Sarayu Sirikonda and Jasper van de Kreeke},
journal= {arXiv preprint arXiv:2509.01763},
year = {2025}
}