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Rule-based Machine Learning: Separating Rule and Rule-Set Pareto-Optimization for Interpretable Noise-Agnostic Modeling

Proceedings of the Genetic and Evolutionary Computation Conference(2025)10.1145/3712256.3726461Source: DataRank Database

Rule-based Machine Learning: Separating Rule and Rule-Set Pareto-Optimization for Interpretable Noise-Agnostic Modeling is a research paper published in Proceedings of the Genetic and Evolutionary Computation Conference (2025). On theSindex it has a DataRank of 0.

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

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Enrichment:Funding (3 grants)Impact (FWCI 0.00)OA: hybridTopics (16)IDs (PubMed, OpenAlex)SDGs (1)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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Authors (5)

Harsh Bandhey ORCID,Ruonan Yin ORCID,Malek Kamoun ORCID,Ryan J. Urbanowicz ORCID,Gabriel Lipschutz-Villa

Live enrichment

PubMed:19767382 OpenAlex:https://openalex.org/W4412106559

NIH (National Institutes of Health)

Grant: AI173095

NIH (National Institutes of Health)

Grant: AG066833

NIH (National Institutes of Health)

Grant: AG0373105

FWCI

0.00

Citation Percentile

0.1%

hybridLicense: cc-by-nc-sa

conferencehttps://doi.org/10.1145/3712256.3726461

publisherhttps://doi.org/10.1145/3712256.3726461

publisherhttps://dl.acm.org/doi/pdf/10.1145/3712256.3726461

Fields of Study

Neural Networks and ApplicationsMachine Learning and Data ClassificationFault Detection and Control Systems

Keywords

Computer scienceRule-based systemArtificial intelligencePareto principleNoise (video)Set (abstract data type)Machine learningLearning ruleData miningPattern recognition (psychology)Mathematical optimizationArtificial neural networkMathematics

Sustainable Development Goals

SDG 0: Reduced inequalities