Learning multi-granularity decision implication in correlative data from a logical perspective
From MaRDI portal
Publication:6596704
DOI10.1016/j.ijar.2024.109250zbMATH Open1547.68738MaRDI QIDQ6596704
Chao Zhang, Yan-Hui Zhai, Deyu Li, Shaoxia Zhang
Publication date: 2 September 2024
Published in: International Journal of Approximate Reasoning (Search for Journal in Brave)
Learning and adaptive systems in artificial intelligence (68T05) Logic in artificial intelligence (68T27) Knowledge representation (68T30)
Cites Work
- Unnamed Item
- Unnamed Item
- Relational concept analysis: mining concept lattices from multi-relational data
- Concept learning via granular computing: a cognitive viewpoint
- Decision implication canonical basis: a logical perspective
- A comparative study of decision implication, concept rule and granular rule
- Knowledge reduction in formal contexts using non-negative matrix factorization
- Three-way cognitive concept learning via multi-granularity
- Rule acquisition and complexity reduction in formal decision contexts
- A novel cognitive system model and approach to transformation of information granules
- Comparison of reduction in formal decision contexts
- Relational concept discovery in structured datasets
- The structure theorem of three-way concept lattice
- Study of decision implications based on formal concept analysis
- A Proposal for Combining Formal Concept Analysis and Description Logics for Mining Relational Data
- Formal concept analysis approach to understand digital evidence relationships
This page was built for publication: Learning multi-granularity decision implication in correlative data from a logical perspective