New uncertainty measurement for hybrid data and its application in attribute reduction
From MaRDI portal
Publication:6497356
DOI10.1016/J.INS.2024.120334MaRDI QIDQ6497356
Ching-Feng Wen, Zhaowen Li, Fang Liu, Haixin Huang
Publication date: 6 May 2024
Published in: Information Sciences (Search for Journal in Brave)
Related Items (1)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- A fuzzy rough set approach for incremental feature selection on hybrid information systems
- Determining the number of clusters using information entropy for mixed data
- Entropy measures and granularity measures for set-valued information systems
- An entropy-based uncertainty measurement approach in neighborhood systems
- Data mining. Concepts and techniques
- Relative reducts in consistent and inconsistent decision tables of the Pawlak rough set model
- Tabu search for attribute reduction in rough set theory
- Neighborhood rough set based heterogeneous feature subset selection
- Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
- Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic
- Rules in incomplete information systems
- Attribute reduction based on D-S evidence theory in a hybrid information system
- Granular computing and dual Galois connection
- Fuzzy rough sets and multiple-premise gradual decision rules
- Information entropy, rough entropy and knowledge granulation in incomplete information systems
- Rough sets
This page was built for publication: New uncertainty measurement for hybrid data and its application in attribute reduction