Attribute reduction based on D-S evidence theory in a hybrid information system
From MaRDI portal
Publication:2169197
DOI10.1016/j.ijar.2022.06.002OpenAlexW4283385768MaRDI QIDQ2169197
Zhaowen Li, Liangdong Qu, Qinli Zhang
Publication date: 2 September 2022
Published in: International Journal of Approximate Reasoning (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.ijar.2022.06.002
D-S evidence theory\( \theta \)-belief reduction\( \theta \)-plausibility reduction\( \theta \)-reductionHIS
Related Items (1)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- A fuzzy rough set approach for incremental feature selection on hybrid information systems
- Entropy measures and granularity measures for set-valued information systems
- A fuzzy similarity-based rough set approach for attribute selection in set-valued information systems
- Neighborhood rough set based heterogeneous feature subset selection
- Information granules and entropy theory in information systems
- Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic
- An information fusion approach by combining multigranulation rough sets and evidence theory
- Belief functions and rough sets: survey and new insights
- Dynamical updating fuzzy rough approximations for hybrid data under the variation of attribute values
- Entropy measures and granularity measures for interval and set-valued information systems
- Granular computing and dual Galois connection
- Attribute reduction based on evidence theory in incomplete decision systems
- Knowledge reduction in random information systems via Dempster-Shafer theory of evidence
- Connections between rough set theory and Dempster-Shafer theory of evidence
- Rough sets
- Upper and Lower Probabilities Induced by a Multivalued Mapping
This page was built for publication: Attribute reduction based on D-S evidence theory in a hybrid information system
