Optimal granularity selection based on algorithm stability with application to attribute reduction in rough set theory

DOI10.1016/j.ins.2023.119845OpenAlexW4388460378MaRDI QIDQ6151917

No author found.

Publication date: 12 February 2024

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2023.119845

zbMATH Keywords

rough setstability theoryattribute reductionoptimal granularity

Mathematics Subject Classification ID

Computer science (68-XX) Operations research, mathematical programming (90-XX)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Pessimistic rough set based decisions: a multigranulation fusion strategy
• Theory and applications of granular labelled partitions in multi-scale decision tables
• Covering based rough set approximations
• Neighborhood rough set based heterogeneous feature subset selection
• Hierarchical neighborhood entropy based multi-granularity attribute reduction with application to gene prioritization
• Uncertainty learning of rough set-based prediction under a holistic framework
• Rough set-based feature selection for weakly labeled data
• MGRS: a multi-granulation rough set
• Multiple granulation rough set approach to ordered information systems
• Rough sets
• Scale-sensitive dimensions, uniform convergence, and learnability
• 10.1162/153244302760200704
• THE ALGORITHM ON KNOWLEDGE REDUCTION IN INCOMPLETE INFORMATION SYSTEMS
• Empirical risk minimization for dominance-based rough set approaches
• A novel variable precision rough set attribute reduction algorithm based on local attribute significance

This page was built for publication: Optimal granularity selection based on algorithm stability with application to attribute reduction in rough set theory