Dynamic interaction feature selection based on fuzzy rough set
DOI10.1016/j.ins.2021.10.026OpenAlexW3204670138MaRDI QIDQ6139519
Xiao-ling Yang, Tianrui Li, Binbin Sang, Hongmei Chen, Jihong Wan
Publication date: 19 January 2024
Published in: Information Sciences (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.ins.2021.10.026
fuzzy rough setfeature interactioninformation measuresfeature selectionmixed datadynamic feature weight
Computing methodologies for image processing (68U10) Theory of fuzzy sets, etc. (03E72) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08) Computational aspects of data analysis and big data (68T09)
Cites Work
- Unnamed Item
- Unnamed Item
- A fuzzy rough set approach for incremental feature selection on hybrid information systems
- Entropy on intuitionistic fuzzy sets and on interval-valued fuzzy sets
- Interval-valued fuzzy strong S-subsethood measures, interval-entropy and P-interval-entropy
- Attribute selection with fuzzy decision reducts
- Fuzzy-rough attribute reduction via mutual information with an application to cancer classification
- Theoretical and empirical analysis of ReliefF and RReliefF
- A comprehensive study of implicator-conjunctor-based and noise-tolerant fuzzy rough sets: definitions, properties and robustness analysis
- A novel fuzzy rough set model with fuzzy neighborhood operators
- Granular fuzzy rough sets based on fuzzy implicators and coimplicators
- Feature selection in mixed data: a method using a novel fuzzy rough set-based information entropy
- Fuzzy clustering of mixed data
- Three-way decisions of rough vague sets from the perspective of fuzziness
- Covering-based variable precision fuzzy rough sets with PROMETHEE-EDAS methods
- Incremental feature selection based on fuzzy rough sets
- A General Framework for Auto-Weighted Feature Selection via Global Redundancy Minimization
This page was built for publication: Dynamic interaction feature selection based on fuzzy rough set
