{"ID":2876079,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.01763","arxiv_id":"2509.01763","title":"A Hybrid Framework for Healing Semigroups with Machine Learning","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2509.01763","url_pdf":"https://arxiv.org/pdf/2509.01763v1","authors":"[\"Sarayu Sirikonda\",\"Jasper van de Kreeke\"]","published":"2025-09-01T20:49:27Z","proceeding":"math.RA","tasks":"[\"math.RA\",\"cs.LG\"]","methods":"[]","has_code":false}