Saved in:
Bibliographic Details
Main Authors: Sirikonda, Sarayu, van de Kreeke, Jasper
Format: Preprint
Published: 2025
Subjects:
Online Access:https://arxiv.org/abs/2509.01763
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1866911133433069568
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