Using tensor products to detect unconditional label dependence in multilabel classifications

DOI10.1016/j.ins.2015.08.055OpenAlexW1433927023WikidataQ62617978 ScholiaQ62617978MaRDI QIDQ1750527

Oscar Luaces, Antonio Bahamonde, Juan José del Coz, Jorge Díez

Publication date: 22 May 2018

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

Full work available at URL: http://hdl.handle.net/10651/35740

zbMATH Keywords

tensor productskernel methodslabel dependencemultilabel

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Uses Software

• LIBSVM
• MULAN
• BoosTexter
• ML-KNN

Cites Work

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• BoosTexter
• Beam search algorithms for multilabel learning
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• Feature selection for multi-label naive Bayes classification
• ML-KNN: A lazy learning approach to multi-label learning
• Efficient Monte Carlo methods for multi-dimensional learning with classifier chains
• BoosTexter: A boosting-based system for text categorization
• Scalable multi-output label prediction: from classifier chains to classifier trellises
• Combining instance-based learning and logistic regression for multilabel classification
• Cutting-plane training of structural SVMs
• Decomposing the tensor kernel support vector machine for neuroscience data with structured labels

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