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Classifier chains for multi-label classification
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Journal Article
Classifier chains for multi-label classification
2011
Overview
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 complexity to model interdependencies between labels. This paper shows that binary relevance-based methods have much to offer, and that high predictive performance can be obtained without impeding scalability to large datasets. We exemplify this with a novel classifier chains method that can model label correlations while maintaining acceptable computational complexity. We extend this approach further in an ensemble framework. An extensive empirical evaluation covers a broad range of multi-label datasets with a variety of evaluation metrics. The results illustrate the competitiveness of the chaining method against related and state-of-the-art methods, both in terms of predictive performance and time complexity.
Publisher
Springer US,Springer,Springer Nature B.V
Subject
/ Algorithmics. Computability. Computer arithmetics
/ Chaining
/ Computer science; control theory; systems
/ Computer systems and distributed systems. User interface
/ Control
/ Data processing. List processing. Character string processing
/ Exact sciences and technology
/ Information systems. Data bases
/ Labels
/ Memory organisation. Data processing
/ Natural Language Processing (NLP)
/ Robotics
/ Software
