Scalable multilabel learning based on feature and label dimensionality reduction

DOI10.1155/2018/6292143zbMath1407.68401OpenAlexW2893237591WikidataQ129202205 ScholiaQ129202205MaRDI QIDQ1632897

Jaesung Lee, Dae-Won Kim

Publication date: 17 December 2018

Published in: Complexity (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2018/6292143

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10) Decentralized systems (93A14) Measures of information, entropy (94A17)

Cites Work

• Multilabel classification with meta-level features in a learning-to-rank framework
• Feature selection for multi-label naive Bayes classification
• Theoretical and empirical analysis of ReliefF and RReliefF
• Fast multi-label feature selection based on information-theoretic feature ranking
• Designing multi-label classifiers that maximize \(F\) measures: state of the art
• Multiple Comparisons Among Means
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