Multilabel classification with meta-level features in a learning-to-rank framework

DOI10.1007/s10994-011-5270-7zbMath1243.68257OpenAlexW2153758664MaRDI QIDQ439034

Siddharth Gopal, Yiming Yang

Publication date: 31 July 2012

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-011-5270-7

zbMATH Keywords

learning to rankmultilabel classification

Mathematics Subject Classification ID

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

Related Items (1)

Scalable multilabel learning based on feature and label dimensionality reduction

Uses Software

• SVMstruct
• MULAN
• BoosTexter
• ML-KNN
• LETOR

Cites Work

• ML-KNN: A lazy learning approach to multi-label learning
• BoosTexter: A boosting-based system for text categorization
• Combining instance-based learning and logistic regression for multilabel classification
• Improved boosting algorithms using confidence-rated predictions
• An optimal algorithm for approximate nearest neighbor searching fixed dimensions
• 10.1162/1532443041827916
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