Adaptive Feature Selection Based on the Most Informative Graph-Based Features

DOI10.1007/978-3-319-58961-9_25zbMath1489.68220OpenAlexW2613223211WikidataQ62523490 ScholiaQ62523490MaRDI QIDQ5082164

Edwin R. Hancock, Lixin Cui, Yuhang Jiao, Luca Rossi, Lu Bai

Publication date: 15 June 2022

Published in: Graph-Based Representations in Pattern Recognition (Search for Journal in Brave)

Full work available at URL: https://eprints.whiterose.ac.uk/114047/1/paper_22.pdf

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Graph theory (including graph drawing) in computer science (68R10) Pattern recognition, speech recognition (68T10) Measures of information, entropy (94A17) Random walks on graphs (05C81)

Related Items (1)

An instance voting approach to feature selection

Cites Work

• Fast depth-based subgraph kernels for unattributed graphs
• A quantum Jensen-Shannon graph kernel for unattributed graphs
• Depth-based hypergraph complexity traces from directed line graphs

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