Classifier chains for multi-label classification

The widely known binary relevance method for multi-label classification, which considers each label as an independent binary problem, has often been overlooked in the literature due to the perceived inadequacy of not directly modelling label correlations. Most current methods invest considerable com...

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Bibliographic Details
Published in:	Machine learning Vol. 85; no. 3; pp. 333 - 359
Main Authors:	Read, Jesse, Pfahringer, Bernhard, Holmes, Geoff, Frank, Eibe
Format:	Journal Article
Language:	English
Published:	Boston Springer US 01.12.2011 Springer Springer Nature B.V
Subjects:	Acceptability Algorithmics. Computability. Computer arithmetics Applied sciences Artificial Intelligence Chaining Classification Classifiers Complexity Computer Science Computer science; control theory; systems Computer systems and distributed systems. User interface Control Correlation Data processing. List processing. Character string processing Exact sciences and technology Information systems. Data bases Labels Mathematical models Mathematical problems Mechatronics Memory organisation. Data processing Natural Language Processing (NLP) Robotics Simulation and Modeling Software Theoretical computing Problem transformation Scalable methods Ensemble methods Multi-label classification High performance Data analysis Automatic classification Scalability Competitiveness Very large databases Computational complexity Modeling Aggregate model Relevance Model matching Supervised learning Metric Time complexity
ISSN:	0885-6125, 1573-0565
Online Access:	Get full text
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