Multi-label feature selection based on the division of label topics

DOI10.1016/j.ins.2020.12.036zbMath1483.68329OpenAlexW3114688077MaRDI QIDQ2123547

Yanyan Li

Publication date: 14 April 2022

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2020.12.036

zbMATH Keywords

spectral clusteringfeature selectionmulti-label learninglabel clusters

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (5)

Multi-label feature selection based on rough granular-ball and label distribution ⋮ Feature relevance and redundancy coefficients for multi-view multi-label feature selection ⋮ Feature selection based on label distribution and fuzzy mutual information ⋮ Feature-specific mutual information variation for multi-label feature selection ⋮ Label correlations variation for robust multi-label feature selection

Uses Software

• Scikit
• MULAN
• ML-KNN
• scikit-multilearn

Cites Work

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• Feature selection for multi-label naive Bayes classification
• ML-KNN: A lazy learning approach to multi-label learning
• Fast multi-label feature selection based on information-theoretic feature ranking
• A novel approach for learning label correlation with application to feature selection of multi-label data
• Noisy label tolerance: a new perspective of partial multi-label learning
• Reference-point-based multi-objective optimization algorithm with opposition-based voting scheme for multi-label feature selection
• A novel attribute reduction approach for multi-label data based on rough set theory
• A fast separability-based feature-selection method for high-dimensional remotely sensed image classification

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