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Data · dataset · 2026

RG-SCSO: source code, seeds, and raw per-run results for relevance-guided parsimonious feature selection

Listed in Teesside University Research Data Repository

This dataset provides the complete, reproducible research artifacts for the study "Relevance-Guided Binarization for Parsimonious Feature Selection with Sand Cat Swarm Optimization" (RG-SCSO).

Description

RG-SCSO replaces the fixed, feature-agnostic transfer function used by swarm-based binary feature selectors with a per-feature, relevance-modulated binarization that injects a mutual-information prior directly at the bit-decision step.

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Where it is published

Catalogue records · 1

Topics

Provenance · 1 source records, 8 field assertions
SourceKeyLast seenRaw
Teesside University Research Data Repositoryoai:data.mendeley.com/7b3d9ryc3c.18 h agoJSON v1
FieldAssertionExtractorEvidence
access_levelsource · researchdata tees ac ukconnector:researchdata_tees_ac_uk@1.0.0
concepts[field].anzsrc:group:4611mapping · researchdata tees ac ukvocabulary-mapper@1.0.0keywords['Machine Learning']
concepts[field].local:field:computer-science-aimapping · researchdata tees ac ukconnector:researchdata_tees_ac_uk@1.0.0
concepts[field].local:field:earth-environmentalmapping · researchdata tees ac ukconnector:researchdata_tees_ac_uk@1.0.0
descriptionsource · researchdata tees ac ukconnector:researchdata_tees_ac_uk@1.0.0/metadata/dc/description
licensesource · researchdata tees ac ukconnector:researchdata_tees_ac_uk@1.0.0/metadata/dc/rights
publication_datesource · researchdata tees ac ukconnector:researchdata_tees_ac_uk@1.0.0
titlesource · researchdata tees ac ukconnector:researchdata_tees_ac_uk@1.0.0/metadata/dc/title