Classifier Chains for Multi-label Classification

Authors:
Jesse Read;Bernhard Pfahringer;Geoff Holmes;Eibe Frank
Affiliations:
Department of Computer Science, The University of Waikato, Hamilton, New Zealand;Department of Computer Science, The University of Waikato, Hamilton, New Zealand;Department of Computer Science, The University of Waikato, Hamilton, New Zealand;Department of Computer Science, The University of Waikato, Hamilton, New Zealand
Venue:
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Year:
2009

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Abstract

The widely known binary relevance method for multi-label classification, which considers each label as an independent binary problem, has been sidelined in the literature due to the perceived inadequacy of its label-independence assumption. Instead, most current methods invest considerable complexity to model interdependencies between labels. This paper shows that binary relevance-based methods have much to offer, especially in terms of scalability to large datasets. We exemplify this with a novel chaining method that can model label correlations while maintaining acceptable computational complexity. Empirical evaluation over a broad range of multi-label datasets with a variety of evaluation metrics demonstrates the competitiveness of our chaining method against related and state-of-the-art methods, both in terms of predictive performance and time complexity.