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arXiv · 2509.01763

A Hybrid Framework for Healing Semigroups with Machine Learning

Sarayu Sirikonda·Jasper van de Kreeke
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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.

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Sarayu Sirikonda, Jasper van de Kreeke. 2025-09-01. A Hybrid Framework for Healing Semigroups with Machine Learning. https://arxiv.org/abs/2509.01763

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