A Hybrid Framework for Healing Semigroups with Machine Learning
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911133433069568 |
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| author | Sirikonda, Sarayu van de Kreeke, Jasper |
| author_facet | Sirikonda, Sarayu van de Kreeke, Jasper |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_01763 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Hybrid Framework for Healing Semigroups with Machine Learning Sirikonda, Sarayu van de Kreeke, Jasper Rings and Algebras Machine Learning 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. |
| title | A Hybrid Framework for Healing Semigroups with Machine Learning |
| topic | Rings and Algebras Machine Learning |
| url | https://arxiv.org/abs/2509.01763 |