Information-theoretic partially labeled heterogeneous feature selection based on neighborhood rough sets

DOI10.1016/j.ijar.2022.12.010OpenAlexW4313256152MaRDI QIDQ6099387

No author found.

Publication date: 20 June 2023

Published in: International Journal of Approximate Reasoning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ijar.2022.12.010

zbMATH Keywords

feature selectionmonotonic entropypartially labeled heterogeneous data

Mathematics Subject Classification ID

Reasoning under uncertainty in the context of artificial intelligence (68T37)

Related Items (1)

Fast attribute reduction via inconsistent equivalence classes for large-scale data

Cites Work

• Unnamed Item
• Local multigranulation decision-theoretic rough sets
• Neighborhood rough set based heterogeneous feature subset selection
• Local rough set: a solution to rough data analysis in big data
• Three-way decision on information tables
• Three-way decision with co-training for partially labeled data
• Feature selection using neighborhood entropy-based uncertainty measures for gene expression data classification
• Attribute reduction with fuzzy rough self-information measures
• Multilabel feature selection using ML-ReliefF and neighborhood mutual information for multilabel neighborhood decision systems
• Rough sets
• Combined SVM-based feature selection and classification

This page was built for publication: Information-theoretic partially labeled heterogeneous feature selection based on neighborhood rough sets